{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# data analysis and wrangling\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random as rnd\n",
    "\n",
    "#visualization\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "#machine learning\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC,LinearSVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.linear_model import Perceptron\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train_df = pd.read_csv(r\"input\\train.csv\")\n",
    "test_df = pd.read_csv(r\"input\\test.csv\")\n",
    "combine = [train_df, test_df]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['PassengerId' 'Survived' 'Pclass' 'Name' 'Sex' 'Age' 'SibSp' 'Parch'\n",
      " 'Ticket' 'Fare' 'Cabin' 'Embarked']\n"
     ]
    }
   ],
   "source": [
    "print(train_df.columns.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#preview the data\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.00</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.00</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                      Name  \\\n",
       "886          887         0       2                     Montvila, Rev. Juozas   \n",
       "887          888         1       1              Graham, Miss. Margaret Edith   \n",
       "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
       "889          890         1       1                     Behr, Mr. Karl Howell   \n",
       "890          891         0       3                       Dooley, Mr. Patrick   \n",
       "\n",
       "        Sex   Age  SibSp  Parch      Ticket   Fare Cabin Embarked  \n",
       "886    male  27.0      0      0      211536  13.00   NaN        S  \n",
       "887  female  19.0      0      0      112053  30.00   B42        S  \n",
       "888  female   NaN      1      2  W./C. 6607  23.45   NaN        S  \n",
       "889    male  26.0      0      0      111369  30.00  C148        C  \n",
       "890    male  32.0      0      0      370376   7.75   NaN        Q  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 66.2+ KB\n",
      "________________________________________\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      "PassengerId    418 non-null int64\n",
      "Pclass         418 non-null int64\n",
      "Name           418 non-null object\n",
      "Sex            418 non-null object\n",
      "Age            332 non-null float64\n",
      "SibSp          418 non-null int64\n",
      "Parch          418 non-null int64\n",
      "Ticket         418 non-null object\n",
      "Fare           417 non-null float64\n",
      "Cabin          91 non-null object\n",
      "Embarked       418 non-null object\n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 27.8+ KB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()\n",
    "print('_'*40)\n",
    "test_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\samsung\\Anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py:3834: RuntimeWarning: Invalid value encountered in percentile\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000         NaN    0.000000   \n",
       "50%     446.000000    0.000000    3.000000         NaN    0.000000   \n",
       "75%     668.500000    1.000000    3.000000         NaN    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>204</td>\n",
       "      <td>889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>891</td>\n",
       "      <td>2</td>\n",
       "      <td>681</td>\n",
       "      <td>147</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Perkin, Mr. John Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>1601</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>577</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>644</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Name   Sex Ticket Cabin Embarked\n",
       "count                      891   891    891   204      889\n",
       "unique                     891     2    681   147        3\n",
       "top     Perkin, Mr. John Henry  male   1601    G6        S\n",
       "freq                         1   577      7     4      644"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe(include=['O'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.629630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.472826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.242363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Survived\n",
       "0       1  0.629630\n",
       "1       2  0.472826\n",
       "2       3  0.242363"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[['Pclass','Survived']].groupby(['Pclass'],\n",
    "                                        as_index=False\n",
    "                                       ).mean().sort_values(\n",
    "    by='Survived',\n",
    "ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>male</td>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sex  Survived\n",
       "0  female  0.742038\n",
       "1    male  0.188908"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[['Sex','Survived']].groupby(['Sex'], as_index=False).mean().sort_values(by=\n",
    "                                                                                'Survived',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.535885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.464286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.345395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SibSp  Survived\n",
       "1      1  0.535885\n",
       "2      2  0.464286\n",
       "0      0  0.345395\n",
       "3      3  0.250000\n",
       "4      4  0.166667\n",
       "5      5  0.000000\n",
       "6      8  0.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[['SibSp','Survived']].groupby(['SibSp'],\n",
    "                                      as_index=False).mean().sort_values(by='Survived',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Parch</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.550847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.343658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Parch  Survived\n",
       "3      3  0.600000\n",
       "1      1  0.550847\n",
       "2      2  0.500000\n",
       "0      0  0.343658\n",
       "5      5  0.200000\n",
       "4      4  0.000000\n",
       "6      6  0.000000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[['Parch','Survived']].groupby(['Parch'],\n",
    "                                      as_index = False).mean().sort_values(by='Survived',ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x8ec7d50>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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HY0iSJHVdx3qaImIP4M3AqzPzVuDWiDgPeBtwRafiSJKk+WV8fJx1626rvP3I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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3b29bf0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(train_df,col = 'Survived')\n",
    "g.map(plt.hist, 'Age', bins = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tzme2etqcDdqdz2yjoc1lMYxsc037NZdOZ5x99immA1u9+m663ZnaN3Xt2LGj\nd4zq73HNmjW7PPf09K5zTU/PMD5TL/t89u3vv1jfd93u9C7LZnfrFvvnZbHPvyttzta0+TaQf/Yd\nlpnTEXEi8FcUT9j7f8BJmXnnPM8hSVJtc037NZfydGB33v7/5jVVWt1HNsPDd5o2qQ3m1UDOzM6s\n17cCz55XIkmSGrazab/mUp4O7L57fzLvc9d5ZDM8vKdpkxZb+25HlCRJkhZRk2OQJc2h6hhIePCx\nqBMTewP1ppiSpFFTp74se7g+YlvNsoEsDUHVMZBQXObdvu1+Tnzmf2a//fZfwHSS1B516ssyx26r\nCTaQpSGpMgYSigbylk315iCVpFFWtb4sc+y2mmADWaqozuU/L/lJejiZnu6yZk29YRLWl2oDG8hS\nRXUu/3nJT9LDSTG93d1Ob6eR1WgDOSJOAj5LMT/yWO/rZzLzxU2eR1psVS//eclP0sON09tplDXd\ng3wE8Dng93nwtvvqfz5KkiRJi6TpBvLhwA2ZOf+Z1SW1Xrfb5e6776617377raTT6ex+Q0mShmwh\nepCvafiYklpqzZp60zHdP3UfJz/vSA444MAFSiZJUn1NN5ADeH5E/E+gA/wt8JbMfKDh80hqiflM\nxyRJUhs11kCOiF8ElgNbgP8GHAq8H1gGvKGp80iSJEkLqbEGcmb+OCIek5nre4u+HxEd4H9HxLmZ\nOdCshp3OeFORGtXP1cZ8C52t2603n+XKlSuHVm51MnY6Y0xOHlo5W6czzvj4GOPjgz/+eXx8jLGx\n6vv0cy5ZMnjGup8X9D+z3Y8LfvBzHatcFlC8t05nvNL7GtQo/Kw+XM67K1U+p/l8TwOsW7eWsTEG\n/j7tb9f/3q76szv7WHX3n2vfcr5hn3uQ/Rbr3Lvbf7Bym2bdurW1f2a63W7ve636/RV1fycNw8Ox\nXm10iEWpcdx3E0UP8qOBewc5xuTk8iYjNa7N+RYq21133cVVX7uByUcNPivl1Iaf8rITV/ALv3DQ\ngmbrq51x7+UcdNBBlc61efMKli1byvLlgz/lbmJiL5YsXVJpH4BNG2HvvZez776PGHifOmUBD35m\nVcpj772XVy4LgC3LlrLPPisqva+q2vyzOmxtLotBstX9nu674/b/YN/H7F/5+3RiYq/aP7vlY9Td\nf3f7TkzstWjn3p299lqyaOeeT7lt33o/X/6XdRxw4HStc99x+3+wZK9lHHDg4yrvW/d30jC1uS5p\nWpNDLH4O8PhLAAAgAElEQVQL+ARwcGb2p3Y7Crg3MwdqHANMTW2h2633jbmQOp1xJieXtzLfQmdb\nv34zSyceyfJHDH4j1tat21m/fjN7771lKOVWJ+P27cXQ+KrZ1q/fzNat29myZfvA+2zb9gA7ppdU\n2qffy7Fx4xbuu29TpXxVywIe/MxWrNj9ufrfcxs3bqlcFlXPVdUo/KwOW5vLYpBsdb+n+5YufQRb\nt+4Y+Pt0fHyMiYm92LbtgVo/u2Xz2X+ufcv5pqfnvji7EOfenX62Bx7YwY7pwcu8iXPvbv9Bym3b\ntgfYa+n8vtc6ey2vtX/d30nD8HCsV5vsQf4GsBn4y4h4B/DLwEXAhVUO0u1Os2NHuwq/rM35Fipb\ntzvN9PTMLivi2Xbs2MHq1cX0X/vss4L16zcP/ENVZ/qvOhn721Ytt7rnGp+pts+D55tZ8HxQZKxe\nFjNDO1dVbf5ZHbY2l8Ug2ep+T/fV/fnrn7Puz+58zj3Ivrsrk4U890D7jy/iuWuW22J/3jD6P697\niibHIN8fEccB7wW+A2wEPpSZ/6upc2h0FI8ZvYf9D9zGsmVL2bp1+0AVhtN/SZKkxdb0GOSbgOOa\nPKZGV/8xo8uXL2XLlsEayJIkSYutfbcjSpIkSYuo6QeFqKJut8vatfWmL6o6VrfuudasWcPMHtj5\nOz1dPCZ5+fLBx0fDnl0eg06l1emMs3nzCtasuWeoZTHI93A/W3ncu4+1liRVYQN5ka1dO7xH9dY9\n1+o7buFRj96fehMstdfGDfdxxVfWse+jV1Ya/rEnl8f//dY97Lf/1t1uOz4+xrJlS7nl5puY3Hfl\n0MpikO/hfrb+uHfHtUuSqrKB3ALDfFRvnXNtuG/dAqVZfHtPFuOkqzSQ9+Ty6I8b353x8TGWL1/K\nmrtXDyHVz9vd93A/m+PeJUl1OQZZkiRJKrEHeUSVx4vubMzlzozC2Nkq42DLRuG91VGUxz173Bhp\nP+fF8U/f+g7rN26utW93R5fnPOMpPPKRj2w4lSSof19MmfdbNKfRBnJETAAfBF5E8dCQ/5WZf9Lk\nOVQojxedPeZyLqMwdrbKONiyUXhvdUyt/ylfvP1+Hrtyy8D7jEJZ+DkvjtXrNrLXow6pte99997D\npk2bbCBLC6TufTF93m/RrKZ7kC8G/ivwLOAQ4LKIuC0zP9vwecSD40UHHXM5KmNnBx0HWzYq762O\nquUxKmXh5yxJP6/OfTFaGI01kCNiBfB7wHGZ+W/Av0XERcDrABvIkvQwNnuKvkGHhoFDa6Q93Xym\nvO10xtl33yc0nKjZHuRf7x3vm6VlXwf+qImDb9w4xWV/+0VWPHKfSvt1pjdzxikvbCKCJKmm2VP0\nDTo0DBxaI+3p6k5DC7Dp/vW8/Vfb3UA+AFiXmTtKy9YAyyLiMZl573wO3u12WbHPAez72IMr7bd5\n3a3zOa0kqSHlKfqqTMfn0Bppz1d3ytvx8bEFSNPsNG8rgG2zlvVfTzR4HkmSJGnBNNmDvJWHNoT7\nrweeV6jT2Xmbfa+9OmzasBZmupVCjT1wP2vX3lNpn53nGmPz5uVs3LiFbre5wXDr1q1l0/3rK/8F\ntHnTFEu2b2fDfWsZHx9jy6a92L79gV32xJT3qXuuOvsMkm1RMt4/xfRMl6VLl1W6IWK+5TGo8fEx\nNm5czwxLFvxcVffrf89tvn+K8SXbhvJ5Dbrf7J+HTfevp9M5lCVLFn/a97nqt2Gdd8e2zdy/9vZa\nx9i0cT0//enejI3Vq/9m13WD1ltQ//ul7v7lbMM+9yD7LnSdP59951s3zOfcu9t/kHJb1M+75u+k\nvk33r2fduuULUs8sVBuor25bCIq6aSGMzTR050NEHA18DViWmdO9Zc8C/i4znRdIkiRJI6HJPzO+\nBzwAPK207BnAdxo8hyRJkrSgGutBBoiIPweOAc4EDgY+BrwsM69q7CSSJEnSAmr6QSHnUjxJ7x+B\nDcCbbRxLkiRplDTagyxJkiSNusW/rVuSJElqERvIkiRJUokNZEmSJKnEBrIkSZJUYgNZkiRJKrGB\nLEmSJJXYQJYkSZJKbCBLkiRJJTaQJUmSpBIbyJIkSVKJDWRJkiSpxAayJEmSVGIDWZIkSSqxgSxJ\nkiSV2ECWJEmSSmwgS5IkSSU2kCVJkqSSJYsdQNVFxG3AL5YWzQD3A/8KvDkzrxvgGM8EvgIckpk/\nXoCYCyIijgAuAp4GdIGvAf9fZt4xx/a/BPwoM+f8YzAiHgW8BXghcBCwAbgOuCAzv9fsO3jIuaeB\nl2fmZQt0/LcBL8vMQxs+7pHAe4EnAWuBP83M9zd5DmlYrFOtUysc/20sQJ1aOv5jgO8Dp2bmtQtx\nDg3GHuTRNAO8B9i/9+9A4GiKSujvI+LgCscZGRHxaOBLFL+4ngE8H9gP+GJELN3Frrt7n1dT/HJ4\nOfCfgN/u7XNdRMQ8Y+/O/sCnFvD4MzT8Ofc+h38AbgaeCLwduDAiXtbkeaQhsk61Th1U43VqX0Qc\nRFG37r8Qx1c19iCPrk2Zubb0ek1EvBq4i+Kv9j2xN++FwArgjMzcDhARpwM/Bp4OfLXqASPiV4Fj\ngSMz8/u9xXdExCnALcDvA2+cf/Sdm/UZjopXAduAV2fmNJARcRhwPvDxRU0m1WedinXqYomIM4EL\ngR8tdhYVbCDvWbq9r9sAImIJxWWuM4DHAjcCb8rML83eMSL2oehBOZ6iB+E+4Crg9Zm5tbfNG4FX\nAwcDq4GPZOY7e+uWU/wC+R1gH+AmistpV+wsaER8FNhZj+MM8PHMPHMn664BTuxX5KXtAfbd2XkG\nMN37+jsUl7UAyMwdEfEbwKZe3odcPp29LCK+QtGr+uvAYcDrgMuBX8nMm/vHjoh/BO7MzDP6lwOB\n23vH2tW2k8DFwEnAUuB64LzM/JfS9q8E/oCiB+xLwG27evMR8SPgl3ayagZ4xRyXKY8FvtZrHPf9\nI3B+RDw2M3+yq3NKI8Q6tTrr1Op1KhR/rLypd45bd3UODYcN5D1E79LMn1JcKvtCb/ElwIuA1wDf\nA34P+FxE/PpODvExigrgJIoxpccAHwVuAC6JiBdQ/PD+N4oK62jgsoi4NTM/AbwT+M8Ul+jWA68E\n/iYinjDHeLzXA+fN8Xa27Gxh7zizj3U+sBmoNVYrM2+KiM8Bf9zrLbqGYqzcNZl5+6zNd3ZZbfay\n3wNOA34A3ENR0Z0GvBV+9jn9BvC8WTm+1qtYd7XtFyk+398Gpih+Sf9TRDw1M/8tIk4FPgCcDXyZ\n4rN/Fw8ts7InAZ051m2YY/nBlH7x9azufX0cYANZI8861Tp1iHUqmfmCXsadNa61CGwgj64/iog/\n6P1/CcVfvzcBJ2fmnRHxSOBM4LWlHodVveFfkzs53j9Q9Ar+e+/1jyPi9cCv9V4/HtgK/Dgz7wT+\nNiLu4sGK4vHARuC2zNwQEW+muDx3387CZ+bG3va1RcTZwFnA2Zl57zwO9UKKXz6nAf8deEXv+J8G\nfj8z769wrO9l5s/Gv0XEZb1jvrW36KUUvRdf2cm+H59r24h4LvBU4Bcyc31v/aqIOBY4h+KzPhv4\nZGb+RW/9RRFxNEXvy07VLLcV9HrUSrYCY8CyGseT2sA61Tp1sepUtZAN5NH1IYreDCguA/60V0H2\nBbAX8M/lnTJzFfzsUlbZnwMnRMQrgCcAvwocQvELAorLWq8Abo6IGyl6Bf5Pr2KHYuzU54CfRMQ/\nU/xy+MSsTA+Gi/hz4PSdrJoBLs/Ms+Z+6xARFwD/E3hHZn5wV9vuTmbOAH8B/EVEPIKih+HFFBUr\nwKkVDvcfs15/HHhrr0finyl+Ycw1TndX2x5FcVPtHbPucVna+wfFL95PzDrmN9hFZR4RNzD35cBX\nZeYnd7JuCzAxa1m/YbxprnNJLWedap0Ki1OnqoVsII+un2bmrsYpPUDRo7dbETEGfB44gqIy+Bvg\nu8CH+9v0/io+svfX828BxwHnRMRbMvOdmfmtiHgcxaWr36S4VPXmiDhujr/s30wxPm9npnaRdQnF\npctTgHPmO7VYRLwQOCIz/xggMzdRXHb7YkT8hGJ84Fx29vPzc5cyM/P2iPgqcFpE3A/8F4rLdA8x\nx7Yv7K0ep7g891956Ofa782d4aEz0zywi/xQjI/ca451a+ZYfgfFpeOyA3vnv2s355PayjrVOrVv\n2HWqWsgG8p7rPyh+kJ9MMeYNgIj4FvBJivFzfUdSjHN7SmZe39tuL4rpeW7pvX4psE+vZ+GbwNsj\n4lKKSvWdUcwN+fXM/Dvg7yLiXODfgd+luFHi52TmOmBdjfd1OcWYvlMz829r7D/bwRS/dD6WmbMb\ndxt4sELbTlGJli+lHjbgOT5K0Rs0RVFGu/olPHvb/h3NN/TOPZGZP+xvHBEfppir9YMUn+kxPNgL\nBsXnP6ecY67T3bgWeFVEjPV6igCeWxwu63ym0iiwTh2Mdar2CDaQ91CZuSUi3k9R0a6jqFj/B8Vl\nvi9Q9Pj1/2q+h6Lif0lv218A/ghYyYOX0pcBF0fEFMUNF48DnsmD0wA9nuKv9FdS/AJ4GsXE+//U\n1HuKiJdTXKZ7I3BtRKwsrd7QvzO8oo9STFv21Yh4K8Uvqr0p5gT9Q+C1ve1+QHEzx5t6YwGfAJw7\n4Dk+A/wZxR3Yu9tnrm3/Hvg34FMRcQ5FL+5rKe5a/+veNu8GrorizvgrKXoyfpcHb6Brykco7ur+\nq4h4D8U4vnMoylHaI1mnDsw6VXsEHxQymgadpPx84DKKsXDfp6h8j8/M/piuGYDMvJuiUjiBYtqi\nTwN3UtzB/aTeNh+hmN7ozRRj6D5FcdnsnN6xzqK4y/d/A0nx8Ig/bHi81ak8OKH/6ln/XlzngL2b\nRY6lmH7pLRS9Cl/vHe/0zLy8tN3pFOPW/p3i/Q1UmWfmFooyHe99LZsZZNssplT7TYppiD5FUbEf\nC5yUmV/tbfMFiptQzqT4vE+imMKoUVlM43YcxZjMf6H4nnhjv6ykEWSdap26aHXqTozUA2f2VGMz\nM/U+h4j4PLAme3MrRsT7KO74nKH4K3qG4k7YeQ32l+ajN2XOrZk517Q7kqQBWafq4aJWD3IUT8Q5\nftbiwynmYDyA4jGJB1BcipUW20A31kiSBmKdqj1e5THIEbEvcBHw7VmrDgcuyhF9zKP2aF6ukqTm\nWKdqj1fnJr2LKcZgHdRfEBF7917fPNdO0mLI4slNXgqUpAZYp+rhotIQi4h4DsWdqBfMWnUExV+U\nqyLijoj4XkSc0VBGSZIkaWgGbiBHxATFk4bOyszZj5kNYJribt3jgb8ELo2IE5sKKkmSJA1DlSEW\nbwO+k5lfmr0iMy+LiM+Vnmd+Q0QcBryGYqqXgczMzMyMjTn2X9IebaiVnPWqpIeBxiu5gad5i4hb\nKSY5n+4t6k92vjUzJ3ey/Wsoept/rUKemampLXS707vfcsg6nXEmJ5fTxnxmq6fN2aDd+cxWTy/b\nsFurraxXR+BzamU2aHc+s9XX5nwjkK3xerVKD/Iz+fnni19EMe74vIh4O/D0zHxeaf1RwA+pqNud\nZseOdhV+WZvzma2eNmeDducz22hoc1mYrb425zNbfW3O1+ZsTRu4gTz7+eIRsRGYycxbI+Jq4Pze\ns+KvpHjK1unAsxrMKkmSJC24Rh41nZnXAycDZ1A8X/11wKmZOXuuZEmSJKnV6syDDEBmvmLW66uB\nq+edSJIkSVpEjfQgS5IkSXsKG8iSJElSiQ1kSZIkqcQGsiRJklRiA1mSJEkqsYEsSZIkldSe5i0i\nPg+sycwze68PAT4MHA3cBrwhM69pIKMkSZI0NLV6kCPiFOD4WYuvBFYDTwQuB66IiIPnF0+SJEka\nrsoN5IjYF7gI+HZp2XOAxwOvysK7gW8CZzYVVJIkSRqGOkMsLgYuAw4qLXsq8N3M3Fpa9nWK4RaS\nJEnSyKjUg9zrKX4GcMGsVQdQDK8oWwM4xEKSJEkjZeAe5IiYAD4EnJWZ2yKivHoFsG3WLtuAiaqB\nOp12TqzRz9XGfGarp83ZoN35zFbPYmVqc1mYrbo25zNbfW3ONwrZmlZliMXbgO9k5pd2sm4r8OhZ\nyyaAzVUDTU4ur7rLULU5n9nqaXM2aHc+s42GNpeF2eprcz6z1dfmfG3O1rQqDeSXACsjYmPv9QRA\nRJwMvAs4Ytb2+wN3Vw00NbWFbne66m4LrtMZZ3JyeSvzma2eNmeDduczWz39bMPW5rIwW3Vtzme2\n+tqcbxSyNa1KA/mZwF6l1xcBM8AfAocA50fERGb2h1ocC1xXNVC3O82OHe0q/LI25zNbPW3OBu3O\nZ7bR0OayMFt9bc5ntvranK/N2Zo2cAM5M+8ov+71JM9k5o8i4nbgDuBjEXEBcALwZODlDWaVJEmS\nFlwjI5szcxo4kWJYxfXAS4GTMvPOJo4vSZIkDUvtR01n5itmvb4VePa8E0mSJEmLqH3zdUiSJEmL\nyAayJEmSVGIDWZIkSSqxgSxJkiSV2ECWJEmSSmwgS5IkSSWVp3mLiF8G/gw4BrgX+EBmXtxb9z7g\nbIon7I31vp6dmR9sLLEkSZK0gCr1IEfEGPB5YA1wJPBqYFVEnNLb5HDgPOAAioeGHAB8pLG0kiRJ\n0gKr2oO8EvhX4KzM3ATcEhFfBo4F/oaigXxRZq5tNqYkSZI0HJUayJl5D3Bq/3VEHAP8BvDqiNgb\nOAi4udGEkiRJ0hDVvkkvIm4DrgW+AXwWOIJizPGqiLgjIr4XEWc0EVKSJEkalso36ZW8iGKc8YeA\n9wL/AkwDNwKXAM8CLo2IDZl51aAH7XTaObFGP1cb85mtnjZng3bnM1s9i5WpzWVhturanM9s9bU5\n3yhka9rYzMzMvA4QEb8LXA5MAo/IzPWldZcAh2Xm8wc83PzCSFL7jQ35fNarkvZ0jderlXqQI2I/\n4OhZPcI3AkuBvTPzp7N2uQl4dpVzTE1todudrrLLUHQ640xOLm9lPrPV0+Zs0O58Zqunn23Y2lwW\nZquuzfnMVl+b841CtqZVHWJxKPDZiDg4M+/uLXsS8BPgnIh4emY+r7T9UcAPq5yg251mx452FX5Z\nm/OZrZ42Z4N25zPbaGhzWZitvjbnM1t9bc7X5mxNq9pA/g5wPfCRiDiXosF8EfBO4FvA+b3lVwLH\nAadTjEWWJEmSRkKlkc2ZOQ2cCGyimL3iUuC9mfmBzLweOBk4A/gB8Drg1Mz8drORJUmSpIVTeRaL\n3lzIJ8+x7mrg6vmGkiRJkhZL++brkCRJkhaRDWRJkiSpxAayJEmSVGIDWZIkSSqxgSxJkiSV2ECW\nJEmSSipP8xYRvwz8GXAMcC/wgcy8uLfuEODDwNHAbcAbMvOapsJKkiRJC61SD3JEjAGfB9YARwKv\nBlZFxCm9Ta4CVgNPBC4HroiIg5uLK0mSJC2sqj3IK4F/Bc7KzE3ALRHxZeDYiFhD8ejpp2bmVuDd\nEfFc4EzgHU2GliRJkhZKpQZy7yl6p/ZfR8QxwDOAs4CnAd/tNY77vk4x3EKSJEkaCbVv0ouI24Br\ngW8CnwUOoBheUbYGcIiFJEmSRsZ8ZrF4EfACirHIfwqsALbN2mYbMDGPc0iSJElDVXkWi77M/C5A\nRJwL/DXwV8C+szabADZXOW6n086Z5/q52pjPbPW0ORu0O5/Z6lmsTG0uC7NV1+Z8ZquvzflGIVvT\nKjWQI2I/4OjMvKq0+EZgKXA3cPisXfbvLR/Y5OTyKpsPXZvzma2eNmeDducz22hoc1mYrb425zNb\nfW3O1+ZsTavag3wo8NmIODgz+w3fJwFrKW7I+4OImMjM/lCLY4HrqpxgamoL3e50xVgLr9MZZ3Jy\neSvzma2eNmeDduczWz39bMPW5rIwW3Vtzme2+tqcbxSyNa1qA/k7wPXAR3pDKw4FLgLeSXHD3h3A\nxyLiAuAE4MnAy6ucoNudZseOdhV+WZvzma2eNmeDducz22hoc1mYrb425zNbfW3O1+ZsTas0cCMz\np4ETgU3AN4BLgfdm5gd6606gGFZxPfBS4KTMvLPZyJIkSdLCqXyTXm8u5JPnWHcr8Oz5hpIkSZIW\nS/tuR5QkSZIWkQ1kSZIkqcQGsiRJklRiA1mSJEkqsYEsSZIkldhAliRJkkqqPmr6QOASiqncNgOf\nBt6Umdsj4n3A2cAMMNb7enZmfrDZyJIkSdLCqToP8meAe4FjgMcAHwV2AOcBh/e+fry0/VQDGSVJ\nkqShGbiBHBEBPAVYmZnresveAryHBxvIF2Xm2oUIKkmSJA1DlTHI9wDP7zeOe8aAR0XE3sBBwM1N\nhpMkSZKGbeAe5MzcAFzTfx0RY8DrgC9R9B7PAKsi4niKYRh/kpmXNRtXkiRJWljzmcXiPcCRwCrg\nV4Bp4EbgeOAvgUsj4sR5J5QkSZKGqOpNegBExIXA64EXZ+aNwI0R8bnMXN/b5IaIOAx4DXBVlWN3\nOu2cea6fq435zFZPm7NBu/OZrZ7FytTmsjBbdW3OZ7b62pxvFLI1bWxmZqbSDhHxfuBVwGmZ+be7\n2O41wFmZ+WsVDl8tjCSNnrEhn896VdKervF6teo8yG8FXgm8JDOvKC1/O/D0zHxeafOjgB9WDTQ1\ntYVud7rqbguu0xlncnJ5K/OZrZ42Z4N25zNbPf1sw9bmsjBbdW3OZ7b62pxvFLI1rco0b4dTjDd+\nF/CNiFhZWn01cH5EnAtcCRwHnA48q2qgbneaHTvaVfhlbc5ntnranA3anc9so6HNZWG2+tqcz2z1\ntTlfm7M1rcrAjRN6268CVvf+3Q2szszrgZOBM4AfUMxucWpmfrvZuJIkSdLCqjLN24XAhbtYfzVF\nT7IkSZI0stp3O6IkSZK0iGwgS5IkSSU2kCVJkqQSG8iSJElSiQ1kSZIkqcQGsiRJklRS9Ul6BwKX\nAM8GNgOfBt6Umdsj4hDgw8DRwG3AGzLzmkbTSpIkSQusag/yZ4BlwDHAKcALgAt6666ieHjIE4HL\ngSsi4uCGckqSJElDUeVR0wE8BViZmet6y94CvCci/h44FHhqZm4F3h0RzwXOBN7RfGxJkiRpYVTp\nQb4HeH6/cVzyKOBpwHd7jeO+r1MMt5AkSZJGRpVHTW8AfjamOCLGgNcBXwYOoBheUbYGcIiFJEmS\nRsp8ZrF4D3AU8D+BFcC2Weu3ARPzOL4kSZI0dJVmseiLiAuB1wMvzswbI2Ir8OhZm01QzHRRSafT\nzpnn+rnamM9s9bQ5G7Q7n9nqWaxMbS4Ls1XX5nxmq6/N+UYhW9MqN5Aj4v3Aq4DTMvPK3uK7gCNm\nbbo/cHfV409OLq+6y1C1OZ/Z6mlzNmh3PrONhjaXhdnqa3M+s9XX5nxtzta0qvMgvxV4JfCSzLyi\ntOpbwHkRMZGZ/aEWxwLXVQ00NbWFbne66m4LrtMZZ3JyeSvzma2eNmeDduczWz39bMPW5rIwW3Vt\nzme2+tqcbxSyNa3KNG+HA6uAdwHfiIiVpdVfA+4APhYRFwAnAE8GXl41ULc7zY4d7Sr8sjbnM1s9\nbc4G7c5nttHQ5rIwW31tzme2+tqcr83ZmlZl4MYJve1XUcxYsZpiCMXqzJwGTqIYVnE98FLgpMy8\ns9m4kiRJ0sKqMs3bhcCFu1h/C8UjqCVJkqSR1b7bESVJkqRFZANZkiRJKrGBLEmSJJXYQJYkSZJK\nbCBLkiRJJTaQJUmSpBIbyJIkSVJJpUdNl0XEBMVDQV6bmdf2lr0POBuYAcZ6X8/OzA82kFWSJEla\ncLUayL3G8SeBI2atOhw4D/h4adlUvWiSJEnS8FVuIEfE4cAn5lh9OHBRZq6dVypJkiRpkdQZg/xM\n4MvA0RTDKACIiL2Bg4Cbm4kmSZIkDV/lHuTM/FD//xFRXnU4xZjjVRFxPHAv8CeZedl8Q0qSJEnD\nUvsmvZ34FWAauBG4BHgWcGlEbMjMqwY9SKfTzok1+rnamM9s9bQ5G7Q7n9nqWaxMbS4Ls1XX5nxm\nq6/N+UYhW9PGZmZmau8cEdPAs0qzWOyTmetL6y8BDsvM5w94yPphJGk0jO1+k0ZZr0ra0zVerzbZ\ng0y5cdxzE/DsKseYmtpCtzvdXKiGdDrjTE4ub2U+s9XT5mzQ7nxmq6efbdjaXBZmq67N+cxWX5vz\njUK2pjXWQI6ItwNPz8znlRYfBfywynG63Wl27GhX4Ze1OZ/Z6mlzNmh3PrONhjaXhdnqa3M+s9XX\n5nxtzta0JnuQrwbOj4hzgSuB44DTKcYiS5IkSSNhviObfza2LTOvB04GzgB+ALwOODUzvz3Pc0iS\nJElDM68e5MzszHp9NUVPsiRJkjSS2jdfhyRJkrSIbCBLkiRJJTaQJUmSpBIbyJIkSVKJDWRJkiSp\nxAayJEmSVFJ7mreImACuB16bmdf2lh0CfBg4GrgNeENmXjP/mJIkSdJw1OpB7jWOPwkcMWvVlcBq\n4InA5cAVEXHw/9/evcdJUtV33P9098z0zOzusLPgXgCNaOJPyEXUGEUw3oJI8gQIIQrBKGK84e2R\nVxI1wUu8PYJ4vxFvKCEab1xjEoMYBaIoiKgE/BFBdGGXGQd2dnZ3pmd2uvv5o6qX2tm5VPVUd5+e\n+b5fL17LVNep8+2q7jNnqk6dWlZCEREREZE2ynwG2cyOBL4wz/JnAY8CnuLuFeA9ZvZs4Gzg7csN\nKp1XrVYZHR1Jvf7GjZvo6dEoHhEREekuzQyxeDpwLXAeMJlY/mTglrhz3HAD0XALWQFGR0f46jW3\nsnZoeMl1d0/s4LTjj+bhD9cFBBEREekumTvI7n5R4//NLPnSFqLhFUkjgHpIK8jaoWGGD97U6Rgi\nIiIiLdP0TXrzGASm5yybBspZNlIqhXlJvpErxHzzZatWq4yMpB8OsWnTJkql0pL1FIsFisVCii3W\nGBsbpa+vxOTkALt2TVGt1pdVf95CPqYQdj5la06nMoW8L5QtuzzafGhNuxvyvgs5G4Sdrxuy5S3P\nDvYrp4sAACAASURBVHIF2DBnWZn9h2EsaWhoILdArRByvmS2++67jyu/cxtDB809JAea2PkgLzp5\nkMMOO2zR9SYnB+nv72NgoG/Jbc5UdnPtD8fYsr2WW/2tEvIxhbDzKVt3GBoa4NIvX82uSpo/biPD\nawqc/ud/0sJUkZCPU8jZoPk2H1rf7oa870LOBmHnCzlb3vLsIN/HgbNabAa2Z9nIxMQU1erSnap2\nK5WKDA0NBJlvvmzj45P0ldcysGbp8cKVygzj45MMDu5ZdL3x8UkqlRmmpmaW3Ob09F56+9ayZt0G\nyuVepqf3UqvNfwY5bf15C/mYQtj5lK05jWztNjExxZ6pOn0H/UbqMrt2/ZIdO1r3neyG4xRiNlh+\nmw+ta3dD3nchZ4Ow83VDtrzl2UG+EXiDmZXdvTHU4jjg+iwbqVZrzM6GtfOTQs6XzFat1qjV6gt2\nSpNqtXqq95V1m8X6Q+suVi5t/a0S8jGFsPMpW3fI8t1tqNXqbdl/IR+nkLNB820+tL7dDXnfhZwN\nws4Xcra85dlB/g6wFficmb0DOAl4EnBWjnWIiIh0XNZpLyGa+rLd91qISHOW20He96equ9fM7GTg\nM0RP2Ps5cIq737vMOkRERIKSZdpLeGjqyy1bDm1xMhHJw7I6yO5emvPz3cAzl5VIRESkC2jaS5GV\nK7z5OkREREREOijPMcgiLdXMo6413k9EutFi7V2pVGRycpDx8cl9MwqMjIxQT38PpogsQR1k6RrN\nPOpa4/1EpBst1t4ViwX6+/uoVGb2zVqxbetdHLRh8wEPIxCR5qiDLF1FY/5EZLVYqL0rFgsMDPQx\nNfVQB3nnjrF2xxNZ0dRBFmq1dI8obdUlvE7XLyIrT2jTsKVt5xrU3ol0ljrIwq6dO/jGjfezcXNl\n0fVadQmv0/WLyMoT2jRsadu5BrV3Ip2VawfZzE4BLiOaH7kQ//s1d39envVI/tasW7/k0IVWXsLr\ndP0isvKENiQrTTvXoPZOpLPyPoN8FHAV8FKiDjJAuj+XRUREREQCkHcH+UjgNnf/dc7bXdH27t3L\nTT/8UarxZv3lPg7dsnG/ZZryR0RERCQ/rTiDfE3O21zx9uzZzc/um2L9wUuPfdt2+03Ub9u237g6\nTfkjIiIikp+8O8gGPNfM/gEoAV8B3uLue3OuZ8UpFIoUU909XThgXJ2m/BERERHJT24dZDN7BDAA\nTAF/ARwBfAToB16fdjulUphPv27kakW+np4ixWKBYrGw5LqFQuGAdRv/P3dZY92lpF13OdtcrEyr\n6i+VivT0LHy8WnlM8xByPmVrTqcylUrp25h9ioVFvz95ZEr+24rtZ3nPyTYjTbZmtp+2/Vpq/eW2\n+Y31l2ojm9EN378Qs0HY+bohW95y6yC7+6/M7GB3H48X/cTMSsA/m9m57p5qROzQ0EBekVqiNflm\nKPf3MjDQt+Sa/f199Pf3zbtuudy73//39PWk2mbadZvZZiNTMls76p/q72P9+kGGh9csue7q/Mzl\nQ9m6w9DQAIODfZRSfHca6tW+VN+f5WrVcZqcHFywrZzPfG3GYtmybj9L+5V2/WbbfMjWRjYj5O9f\nyNkg7HwhZ8tbrkMsEp3jhjuIziBvAB5Is42Jial9N5qFpFQqMjQ00JJ84+N7mK7sZWpqZsl1K5UZ\neioz+61bLBYol3uZnt67b4jF9PReZms9qbaZdt1mtjk9vfeAbO2ov1KZYXx8ksHBPQuu08pjmoeQ\n8ylbcxrZ2m1iYorJyRl6epf+7jTUJmfYsWPh789ytfo4jY9PUpnTVi4m2WakyZZ1+1nar6XWX26b\nD+nayGZ0w/cvxGwQdr5uyJa3PIdYPAf4AnC4uzemdns88IC7p+ocA1SrNWZnw9r5Sa3INztbo1ar\nL9iBTKrX6wuum1xeq9Up1tNtM+26y9nmYu+vVfWnPVar8TOXF2XrDtVq+jamoVart2X/teo4ZX3P\n87UZi2VrZvtp26+06zfb5jfWb+V3JOTvX8jZIOx8IWfLW55nkL8LTAKfNrO3A48GLgDOz7EOkVTS\nPNY1OT3ewQc/rGWPmBXpRrVale3bt2Uq08pHNUu+sj76GrId32q1yvbt21u2fZFWy3MM8m4zOwH4\nIHATsAu4yN3fl1cdImmleaxrY3q8X4+O8OfPflzLHjEr0o127hwP6lHNkq+sj77OenxHRsJ61LdI\nVnmPQb4DOCHPbYo0a6nHujamx6tU0o/LFFlNQntUs+Qry6Ovm6HPj3Sz8ObrEBERERHpoLwfFCKy\nYlWrVUZH043Zq1arAKnG07Vi3F3arFlygsYIysKyjmmN7jx/VAsTZZPMn7w/YaE79kdGRqinv+ex\n66U9vo19NzJyf1D7J2oT71/yuCapvVvd1EEWSWl0NP2Yum1b76Knt5+Nmw9bdL1WjbtLmzVtTtAY\nQVlc1jGte3aP87L1gwwOrm9xsnSS+Rv3J1QqMwvOCrFt610ctGEzG9qcs1PSHt/GvrvrzjsYGt4U\nzP4ZHR3ha9f+mIdt3LTocW1QeyfqIItkkHZM3c4dY/T0DXR0/F2arCHklJUjy5jWTE/1a5NG/sb9\nCVNTC3ekdu4Ya3O6zktzfBv7biTjDCjtsG5omA2HbFr0uIo0qIMsq17aS4etuKS6WN1zL/NmGQ6x\n2i7/ysqXZYgT6DvQbVo97Vy355H2UwdZVr3o0uH2JS8dtuKS6mKXLede5s0yHGK1Xf6VlS/LECfQ\nd6DbtHrauW7PI+2XawfZzMrAx4FTiR4a8j53f3+edYi0QppLh626pLpQ3XMv82YZDrEaL//Kypdl\n2jB9B7pPq6edyyq0PNJeeU/zdiHwBOAZwDnAW83s1JzrEBERERFpmdzOIJvZIPAS4AR3/zHwYzO7\nAHg1cFle9YhIZ2SZBivtWLxWTJ0X2vRhIpK/rGOEozHp4QxKn6/ty6tdlXzkOcTicfH2vpdYdgPw\n9znWISIdknYarCxj8VoxdV5o04eJSP6yjhHetvUu1h+8pcWp0puv7curXZV85NlB3gKMuftsYtkI\n0G9mB7v7AznWJSIdkGUarLTynjovxOnDRCR/WcYIhzgmfW7bl2e7KsuX5xjkQWB6zrLGz+Uc6xER\nERERaZk8zyBXOLAj3Ph5Mu1GSqW87xvMRyNXK/L195fpqY6zd+feJdcd6K2zZ/f4fmfJisUCU3t6\nmZnZu++vzsk9E/TMzLBzx+iS20y7brPbnJut3fUvpLHfJndPUOyZbnv9S60397iGdEzn+8w17Nk9\nztjYQKrvytjY6AGf5+Vm3bNrHAizLelUplKpyNr+Irt23pO6zNqBIr9OeWwg22cJouO0fft21q6d\nolpd+mxZls9KM3nSfr7z2P5y119um9/K9bO2q63Ok1y/d3aGB8dGFj2uzW4/S7sH83+el2pXS6Uj\n6OnpXBuS/DckrcpUyGvQupkdA3wH6Hf3WrzsGcC/ufvaXCoREREREWmxPLvdtwJ7gacklj0NuCnH\nOkREREREWiq3M8gAZvYJ4FjgbOBw4HPAi9z9ytwqERERERFpobwfNX0u0ZP0vgXsBN6szrGIiIiI\ndJNczyCLiIiIiHS78G5HFBERERHpIHWQRUREREQS1EEWEREREUlQB1lEREREJEEdZBERERGRBHWQ\nRUREREQS1EEWEREREUlQB1lEREREJEEdZBERERGRBHWQRUREREQS1EEWEREREUlQB1lEREREJEEd\nZBERERGRBHWQRUREREQS1EEWEREREUlQB1lEREREJKGn0wEkOzO7B3hEYlEd2A38CHizu1+fYhtP\nB/4beKS7/6oFMVvCzJ4AXAD8ATAFXAa8wd0nFlj/N4BfuPuCfwya2UHAW4A/Aw4DdgLXA+9w91vz\nfQcH1F0DznL3S1q0/bcBL3L3I3Le7tHAB4HfB0aBD7j7R/KsQ6Rd1KaqTc2w/bfRgjY1sf2DgZ8A\nZ7j7da2oQ9LRGeTuVAfeC2yO/zsUOIaoEfpPMzs8w3a6hpltBK4B7gaeAJwMPA24eImiS73Pq4Gn\nAGcBvwn8cVzmejOzZUROYzPwpRZuv07Ox9nMNgD/BdwJPBH4R+B8M3tRnvWItJHaVLWpaeXepjaY\n2WFEbevmVmxfstEZ5O61x91HEz+PmNkrgPuI/mpfiWfzHgn8J/AKd68BPzezTwLvanaDZvbbwHHA\n0e7+k3jxVjM7HbgLeCnwN8tKvYg5x7BbvByY5qHj4Gb2GOCNwOc7mkykeWpT1aZ2jJmdDZwP/KLT\nWSSiDvLKUo3/nQYwsx6iy1wvBB4G3A68yd2/Obegma0nOoNyIrAR2AFcCbzW3SvxOn8DvAI4HNgG\nfNbd3xm/NkD0C+RPgPXAHUSX0y6fL6iZXQzMd8axDnze3c+e+4K7/wA4M7GNx8bv7RsL7pGl1eJ/\n/4Toslajrlkz+0NgT1zXAZdP5y4zs/8mOqv6OOAxwKuBS4HHuvudidzfAu519xc2LgcCv4y3tdi6\nQ8CFwClAH3Az0aXQHybWfxnwt0RnwL4J3LPYmzezXwC/Mc9LdeDFC1ymPA74TvwLteFbwBvN7GHu\n/uvF6hTpImpTs1Obmr1NheiPsDfFddy9WB3SHuogrxDxpZkPEI2b+/d48YeBU4FXArcCLwGuMrPH\nzbOJzxE1AKcQjSk9lugy223Ah83sT4m+vH9B1GAdA1xiZne7+xeAdwK/AzwXGAdeBvyrmf3WAuPx\nXgu8YYG3M5Xi/TrwW0SN1SlLrb8Qd7/DzK4C3hWfLbqGaKzcNe7+yzmrz3dZbe6ylxD9wvkpcD9R\nQ3cm8NY492HAHwLHz8nxnbhhXWzd/yA6vn8MTBD9IvsfM3uyu//YzM4APgq8BriW6Ni/G1hsPOTv\nA6UFXtu5wPLDSfzii22L/304oA6ydD21qc1Rm9pUm4q7/2mccb7OtXSAOsjd6+/N7G/j/+8h+uv3\nDuA0d7/XzNYCZwOvSpxxOC8e/jU0z/b+i+is4P/GP//KzF4L/G7886OACvArd78X+IqZ3cdDDcWj\ngF3APe6+08zeDHyb6KzJAdx9V7x+s84A1hCdofm2mf2eu082ua0/I/rlcybwV8CLAczsy8BL3X13\nhm3d6u77xr+Z2SXxNt8aL/pLorMX/z1P2c8vtK6ZPRt4MnCIu4/Hr59nZscBryM61q8Bvuju/xS/\nfoGZHUN09mVe7v5AhvfWMEh8Ri2hAhSA/ia2JxICtalqUzvVpkqA1EHuXhcRnc2A6DLgg3ED2WBA\nL/D9ZCF3Pw/2XcpK+gRwkpm9mOgswm8TjU+7I379UqJG7k4zu53orMBX44YdorFTVwG/NrPvE/1y\n+MKcTA+FM/sE8IJ5XqoDl7r7OQu/dXD3W+Lt/BlwL9Ff9pcuVmaRbdWBfwL+yczWEJ1heB5RwwrR\nL460/m/Oz58H3hqfkfg+0S+MhcbpLrbu44luqt065x6Xvvg/iH7xfmHONr/LIo25md3GwpcDX+7u\nX5zntSmgPGdZo2O8Z6G6RAKnNhW1qXSmTZUAqYPcvR5098XGKe0lOqO3JDMrAF8HjiJqDP4VuAX4\nVGOd+K/io+O/np8DnAC8zsze4u7vdPcbzezhRJeu/ojoUtWbzeyEBf6yfzPRmYr5LDS90GOA33T3\nxuVO3H27mT1ANJVQZvEvg6Pc/V3x9vYQXXb7DzP7NdH4wIXM9/3Z71Kmu//SzL4NnGlmu4HfI/rF\nc4AF1v2z+OUi0eW5J3DgcW2cza1z4Mw0exfJD9H4yN4FXhtZYPlWokvHSYfG9d+3RH0ioVKbitrU\nWLvbVAmQOsgr1/8RfZGfRDTmDQAzuxH4ItH4uYajica5/YG73xyv10s0Pc9d8c9/Cax3948D3wP+\n0aK7nU8H3mnR3JA3uPu/Af9mZucC/wv8OdGNEvtx9zFgLON7Oh54r5lt9niOTjN7NHBIXFczDif6\npfM5d5/budvJQw3aDFEjmryU+piUdVxMdDZogmgfLfZLeO66jTuab4vrLrv7zxorm9mniOZq/TjR\nMT2Wh86CQXT8F+TuW1O+h6TrgJebWSE+UwTw7GhznvWYinQLtanpqE2VFUEd5BXK3afM7CNEDe0Y\nUWP310SX+f6d6Ixf46/m+4ka/ufH6x4C/D2wiYcupfcDF5rZBNENFw8Hnk40Jg6i8XJnWnTH711E\nc2A+AvifHN/WF4huQrnUzN4IbCBquG4kOlvTjIuJpi37tpm9legX1TqiuUD/DnhVvN5PiW7meFM8\nFvC3gHNT1vE14GNEd2AvVWahdf8T+DHwJTN7HdFZ3FcR3bX+L/E67wGutOjO+CuIzmT8OQ/dQJeX\nzxLd1f0ZM3sv0Ti+1xHtR5EVSW1qampTZUXQg0K6U9pJyt8IXEI0Fu4nRI3vie7eGNNVh+iSGlGj\ncBLRtEVfJhqD9gGiO3Jx988STW/0ZqIxdF8iumz2unhb5xDd5fvPgBM9POLv8hxv5e47gGfFP94A\nXE40Lc9zE2cys25zN9G0ZVcSvb/b4m0/D3iBu1+aWO8FROPW/pfo/aVqzN19imifFuN/k+pp1vVo\nSrU/Inq/XyJq2I8DTnH3b8fr/DvRTShnEx3vU4imMMqVR9O4nUA0JvOHRJ+Jv2nsK5EupDZVbWrH\n2tR5dNUDZ1aqQr3e3HEws68DIx7PrWhmHyK647NO9Fd0HXhNfPlIpCPiKXPudveFpt0REZGU1KbK\natHUGWSLnohz4pzFRxJdqtlC9JjELUSXYkU6LdWNNSIikoraVFnxMo9BNrNh4ALgB3NeOhK4wLv0\nMY+youlylYhIftSmyorXzE16FxKNwdo3BYyZrYt/vnOhQiKd4NGTm3QpUEQkB2pTZbXINMTCzJ5F\ndCfqO+a8dBTRX5TnmdlWM7vVzF6YU0YRERERkbZJ3UE2szLRk4bOcfe5j5k1oEZ0t+6JwKeBT5rZ\nyXkFFRERERFphyxDLN4G3OTu35z7grtfYmZXJZ5nflv8hJ5XEk31IiIiIiLSFVJP82ZmdxNNcl6L\nFzUmO6+4+9A867+S6Gzz76YNU6/X64WCbo4VkRWtrY2c2lURWQVyb+SynEF+Ovs/X/wConHHbzCz\nfwSe6u7HJ15/PPAzMigUCkxMTFGt1pZeuc1KpSJDQwNB5lO25oScDcLOp2zNaWRrp1Db1W44TiFm\ng7DzKVvzQs7XDdnylrqDPPf54ma2C6i7+91mdjXwxvhZ8VcQPWXrBcAzsgaqVmvMzoa185NCzqds\nzQk5G4SdT9m6Q8j7QtmaF3I+ZWteyPlCzpa3XB417e43A6cBLyR6vvqrgTPcfe5cySIiIiIiQWtm\nHmQA3P3Fc36+Grh62YlERERERDoolzPIIiIiIiIrRdNnkKWzqtUqo6MjQDRAfXJykPHxyVSD5zdu\n3ESppAchiYiIiMxHHeQuNTo6wlevuZW1Q8MUiwX6+/uoVGao1Raftm/3xA5OO/5otmw5tE1JRURE\nRLqLOshdbO3QMMMHb6JYLDAw0MfU1NIdZBERERFZnMYgi4iIiIgkqIMsIiIiIpKgDrKIiIiISELT\nY5DN7OvAiLufHf/8SOBTwDHAPcDr3f2aHDKKiIiIiLRNU2eQzex04MQ5i68AtgFPBC4FLjezw5cX\nT0RERESkvTJ3kM1sGLgA+EFi2bOARwEv98h7gO8BZ+cVVERERESkHZoZYnEhcAlwWGLZk4Fb3L2S\nWHYD0XALEREREZGukekMcnym+GnAO+a8tIVoeEXSCKAhFiIiIiLSVVKfQTazMnARcI67T5tZ8uVB\nYHpOkWmgnDVQqRTmxBqNXKHkK5WKFIuFff8B+/5dTLFYoFQq0tPTnvcR2n5LCjkbhJ1P2ZrTqUwh\n7wtlyy7kfMrWvJDzdUO2vGUZYvE24CZ3/+Y8r1WADXOWlYHJrIGGhgayFmmrUPJNTg7S39/HwEDf\nvmXlcu+S5ab6+1i/fpDh4TWtjHeAUPbbfELOBmHnU7buEPK+ULbmhZxP2ZoXcr6Qs+UtSwf5+cAm\nM9sV/1wGMLPTgHcDR81ZfzOwPWugiYkpqtVa1mItVyoVGRoaCCbf+PgklcoMU1MzFIsFyuVepqf3\nLvmo6UplhvHxSQYH97QlZ2j7LSnkbBB2PmVrTiNbu4W8L5Qtu5DzKVvzQs7XDdnylqWD/HQgeYry\nAqAO/B3wSOCNZlZ298ZQi+OA67MGqlZrzM6GtfOTQslXrdao1er7dYjn/jyfWq3ekfcQyn6bT8jZ\nIOx8ytYdQt4Xyta8kPMpW/NCzhdytryl7iC7+9bkz/GZ5Lq7/8LMfglsBT5nZu8ATgKeBJyVY9YV\nqVqtMjo6krncyMgI9cX7wiIiIiLShKafpJfk7jUzOxn4DHAz8HPgFHe/N4/tr2SjoyN89ZpbWTs0\nnKnctq13cdCGzQcM/BYRERGR5Wm6g+zuL57z893AM5edaBVaOzTM8MGbMpXZuWOsRWlEREREVrfw\n5usQEREREekgdZBFRERERBLUQRYRERERSVAHWUREREQkQR1kEREREZEEdZBFRERERBIyT/NmZo8G\nPgYcCzwAfNTdL4xf+xDwGqIn7BXif1/j7h/PLbGIiIiISAtlOoNsZgXg68AIcDTwCuA8Mzs9XuVI\n4A3AFmBz/O9nc0srIiIiItJiWc8gbwJ+BJzj7nuAu8zsWuA44F+JOsgXuPtovjElL7ValZGR7I+2\nbti4cROlUinHRCIiIiJhydRBdvf7gTMaP5vZscAfAq8ws3XAYcCduSaUXO3auYNv3Hg/GzdXMpfd\nPbGD044/mi1bDm1BMhEREZEwNP2oaTO7B3g48G/AZcAfEI05Ps/MTiQan/x+d79k+TElT2vWrc/8\naGsRERGR1aLpDjJwKtE444uADwI/BGrA7cCHgWcAnzSzne5+ZdqNlkphTqzRyJV3vlKpSLFYoFgs\nZCpXLBYoFAr7lU2zjWS5rIrFAqVSkZ6e9PugVfstDyFng7DzKVtzOpUp5H2hbNmFnE/Zmhdyvm7I\nlremO8jufguAmb0euBQYAq5y9/F4ldvM7DHAK4HUHeShoYFmI7VF3vkmJwfp7+9jYKAvU7lyuZee\nvp79ypXLvU2VS2uqv4/16wcZHl6TuWzIxzXkbBB2PmXrDiHvC2VrXsj5lK15IecLOVveMnWQzWwj\ncMycM8K3A33AOnd/cE6RO4BnZqljYmKKarWWpUhblEpFhoYGcs83Pj5JpTLD1NRMpnLT03uZrfUw\nNTVDsVigXO5lenovtVo9dbmsKpUZxscnGRzck7pMq/ZbHkLOBmHnU7bmNLK1W8j7QtmyCzmfsjUv\n5HzdkC1vWc8gHwFcZmaHu/v2eNnvA78GXmdmT3X34xPrPx74WZYKqtUas7Nh7fykvPNVqzVqtfqS\nHdu5arU6xfr+5dJsZ75yWeps9v2HfFxDzgZh51O27hDyvlC25oWcT9maF3K+kLPlLWsH+SbgZuCz\nZnYuUYf5AuCdwI3AG+PlVwAnAC8gGossIiIiItIVMo1sdvcacDKwB/gu8Engg+7+UXe/GTgNeCHw\nU+DVwBnu/oN8I4uIiIiItE7mm/TiuZBPW+C1q4GrlxtKRERERKRTwpuvQ0RERESkg9RBFhERERFJ\nUAdZRERERCRBHWQRERERkYTlPGpaVplarcrIyEimMqVSkcnJQcrldUD2x1uLiIiItJs6yJLarp07\n+MaN97NxcyV1mWKxwMz0bk5++u+wcePmFqYTERERyUfmDrKZPRr4GHAs8ADwUXe/MH7tkcCngGOA\ne4DXu/s1eYWVzluzbj3DB29KvX6xWGBqT18LE4mIiIjkK9MYZDMrAF8HRoCjgVcA55nZ6fEqVwLb\ngCcClwKXm9nh+cUVEREREWmtrGeQNwE/As5x9z3AXWZ2LXCcmY0QPXr6ye5eAd5jZs8Gzgbenmdo\nEREREZFWydRBjp+id0bjZzM7FngacA7wFOCWuHPccAPRcAsRERERka7Q9DRvZnYPcB3wPeAyYAvR\n8IqkEUBDLERERESkayxnFotTgc3AJ4APAIPA9Jx1poFylo2WSmFOzdzIlXe+UqlIsVigWMw2BVqx\nWKBQKOxXNs02kuWyaqZsY91SqUBPT1jHtlXHNC8h51O25nQqU8j7QtmyCzmfsjUv5HzdkC1vTXeQ\n3f0WADM7F/gX4DPA8JzVysBklu0ODQ00G6kt8s43OTlIf38fAwPZZnool3vp6evZr1y53NtUueXU\nmcaeXbBu3QDDw2sy19kOq+0zlydl6w4h7wtla17I+ZSteSHnCzlb3jJ1kM1sI3CMu1+ZWHw70Ads\nB46cU2RzvDy1iYkpqtValiJtUSoVGRoayD3f+PgklcoMU1MzmcpNT+9lttbD1NQMxWKBcrmX6em9\n1Gr11OWyaqZs4wzyrl1T7NixJ3OdrdSqY5qXkPMpW3Ma2dot5H2hbNmFnE/Zmhdyvm7IlresZ5CP\nAC4zs8PdvdHx/X1glOiGvL81s7K7N4ZaHAdcn6WCarXG7GxYOz8p73zVao1arb5kx3auWq1Osb5/\nuTTbma/ccupMq1qtB3tcV9tnLk/K1h1C3hfK1ryQ8ylb80LOF3K2vGXtIN8E3Ax8Nh5acQRwAfBO\nohv2tgKfM7N3ACcBTwLOyi2tiIiIiEiLZRrZ7O414GRgD/Bd4JPAB939o/FrJxENq7gZ+EvgFHe/\nN9/IIiIiIiKtk/kmvXgu5NMWeO1u4JnLDSUiIiIi0inhzdchIiIiItJB6iCLiIiIiCSogywiIiIi\nkqAOsoiIiIhIgjrIIiIiIiIJ6iCLiIiIiCRkfdT0ocCHiaZymwS+DLzJ3WfM7EPAa4A6UIj/fY27\nfzzfyCIiIiIirZN1HuSvAQ8AxwIHAxcDs8AbgCPjfz+fWH8ih4wiIiIiIm2TuoNsZgb8AbDJ3cfi\nZW8B3stDHeQL3H20FUFFRERERNohyxjk+4HnNjrHsQJwkJmtAw4D7swznIiIiIhIu6U+g+zu7tLX\nwQAAH95JREFUO4FrGj+bWQF4NfBNorPHdeA8MzuRaBjG+939knzjioiIiIi01nJmsXgvcDRwHvBY\noAbcDpwIfBr4pJmdvOyEIiIiIiJtlPUmPQDM7HzgtcDz3P124HYzu8rdx+NVbjOzxwCvBK7Msu1S\nKcyZ5xq58s5XKhUpFgsUi4VM5YrFAoVCYb+yabaRLJdVM2Ub65ZKBXp6wjq2rTqmeQk5n7I1p1OZ\nQt4XypZdyPmUrXkh5+uGbHnL3EE2s48ALwfOdPcrGssTneOGO4img8tkaGgga5G2yjvf5OQg/f19\nDAz0ZSpXLvfS09ezX7lyubepcsupM409u2DdugGGh9dkrrMdVttnLk/K1h1C3hfK1ryQ8ylb80LO\nF3K2vGWdB/mtwMuA57v75Ynl/wg81d2PT6z+eOBnWQNNTExRrdayFmu5UqnI0NBA7vnGxyepVGaY\nmprJVG56ei+ztR6mpmYoFguUy71MT++lVqunLpdVM2UbZ5B37Zpix449metspVYd07yEnE/ZmtPI\n1m4h7wtlyy7kfMrWvJDzdUO2vGWZ5u1IovHG7wa+a2abEi9fDbzRzM4FrgBOAF4APCNroGq1xuxs\nWDs/Ke981WqNWq2+ZMd2rlqtTrG+f7k025mv3HLqTKtarQd7XFfbZy5PytYdQt4Xyta8kPMpW/NC\nzhdytrxlGbhxUrz+ecC2+L/twDZ3vxk4DXgh8FOi2S3OcPcf5BtXRERERKS1skzzdj5w/iKvX010\nJllEREREpGuFdzuiiIiIiEgHqYMsIiIiIpKgDrKIiIiISII6yCIiIiIiCeogi4iIiIgkqIMsIiIi\nIpKQ9Ul6hwIfJnqE9CTwZeBN7j5jZo8EPgUcA9wDvN7dr8k1rYiIiIhIi2U9g/w1oB84Fjgd+FPg\nHfFrVxI9POSJwKXA5WZ2eE45RURERETaIsujpg34A2CTu4/Fy94CvNfM/hM4Aniyu1eA95jZs4Gz\ngbfnH1tEREREpDWynEG+H3huo3OccBDwFOCWuHPccAPRcAsRERERka6R5VHTO4F9Y4rNrAC8GrgW\n2EI0vCJpBNAQCxERERHpKsuZxeK9wOOBfwAGgek5r08D5WVsX0RERESk7TLNYtFgZucDrwWe5+63\nm1kF2DBntTLRTBeZlEphzjzXyJV3vlKpSLFYoFgsZCpXLBYoFAr7lU2zjWS5rJopWywWqNWqjI2N\nZK4PYNOmTZRKpabKLqVVxzQvIedTtuZ0KlPI+0LZsgs5n7I1L+R83ZAtb5k7yGb2EeDlwJnufkW8\n+D7gqDmrbga2Z93+0NBA1iJtlXe+yclB+vv7GBjoy1SuXO6lp69nv3Llcm9T5ZZTZxr3jT/If91f\nYcuh1UzlJnY+yItOHuSwww7LVC6r1faZy5OydYeQ94WyNS/kfMrWvJDzhZwtb1nnQX4r8DLg+e5+\neeKlG4E3mFnZ3RtDLY4Drs8aaGJiimq1lrVYy5VKRYaGBnLPNz4+SaUyw9TUTKZy09N7ma31MDU1\nQ7FYoFzuZXp6L7VaPXW5rJop2zjbXO5fx8Ca4Uz1VSozjI9PMji4J1O5tFp1TPMScj5la04jW7uF\nvC+ULbuQ8ylb80LO1w3Z8pZlmrcjgfOAdwPfNbNNiZe/A2wFPmdm7wBOAp4EnJU1ULVaY3Y2rJ2f\nlHe+arVGrVZfsmM7V61Wp1jfv1ya7cxXbjl1ZinbzHtsdn9Xq1VGRxcf1lEqFVm/fpDx8cn9vvAb\nN7ZuWEczQv5OKFt3CHlfKFvzQs6nbM0LOV/I2fKW5QzySUQ39Z0X/wdQAOruXjKzU4BPAzcDPwdO\ncfd78wwrktbo6AhfveZW1g4tfNa6WCzQ399HpTKzr/O+e2IHpx1/NFu2HNquqCIiIhKYLNO8nQ+c\nv8jrdxE9glokCGuHhhk+eNOCrxeLBQYG+piammnqrLiIiIisTOHdjigiIiIi0kHqIIuIiIiIJKiD\nLCIiIiKSoA6yiIiIiEiCOsgiIiIiIglNPWpapB1qtSojI809onpkZIS6JqYQERGRJjTdQTazMtGc\nx69y9+viZR8CXgPUiedIBl7j7h/PIausMrt27uAbN97Pxs2VzGW3bb2LgzZsZkMLcomIiMjK1lQH\nOe4cfxE4as5LRwJvAD6fWDbRXDQRWLNu/aJzGS9k546xFqQRERGR1SBzBzl+5PQXFnj5SOACdx9d\nVioRERERkQ5p5ia9pwPXAscQDaMAwMzWAYcBd+YTTURERESk/TKfQXb3ixr/b2bJl44kGnN8npmd\nCDwAvN/dL1luSBERERGRdslzmrfHAjXgduBE4NPAJ83s5BzrEBERERFpqdymeXP3S8zsKncfjxfd\nZmaPAV4JXJl2O6VSmFMzN3Llna9UKlIsFigWC0uvnFAsFigUCvuVTbONZLmsmimbzLac95hVmrLz\n7bdisUCpVKSnp/Ofw1Z95vKgbM3pVKaQ94WyZRdyPmVrXsj5uiFb3nKdBznROW64A3hmlm0MDQ3k\nF6gF8s43OTlIf38fAwN9mcqVy7309PXsV65c7m2q3HLqTKuvr5TLe2xF2eR+m+rvY/36QYaH12Su\ns1VC/k4oW3cIeV8oW/NCzqdszQs5X8jZ8pZbB9nM/hF4qrsfn1j8eOBnWbYzMTFFtVrLK1ZuSqUi\nQ0MDuecbH5+kUplhamomU7np6b3M1nqYmpqhWCxQLvcyPb2XWm3xp2Mky2XVTNnG2dmZmeqy3mNW\nacrOt98qlRnGxycZHNyTuc68teozlwdla04jW7uFvC+ULbuQ8ylb80LO1w3Z8pbnGeSrgTea2bnA\nFcAJwAuAZ2TZSLVaY3Y2rJ2flHe+arVGrVZfsmM7V61Wp1jfv1ya7cxXbjl1Zimbx3tsRdlktlqt\nHtxnMLQ8ScrWHULeF8rWvJDzKVvzQs4Xcra8LXfgxr7eh7vfDJwGvBD4KfBq4Ax3/8Ey6xARERER\naZtlnUF299Kcn68mOpMsIiIiItKVwrsdUURERESkg9RBFhERERFJUAdZRERERCRBHWQRERERkQR1\nkEVEREREEnJ9kl6eqtUqN3zvB01NSL1p4yH89pHWglQiIiIistIF20Heu3cvd23fw4bNj8pcduKe\nX6mDLCIiIiJNabqDbGZl4GbgVe5+XbzskcCngGOAe4DXu/s1y48pIiIiItIeTXWQ487xF4Gj5rx0\nBfBj4InAnwGXm9lj3f3eZaVsk2q1yujoyLyvlUpFJicHGR+fnHfYx8aNmyiVSvOUFBEREZFukrmD\nbGZHAl+YZ/mzgEcBT3H3CvAeM3s2cDbw9uUGbYfR0RG+es2trB0aPuC1YrFAf38flcoMtVp9v9d2\nT+zgtOOPZsuWQ9sVVURERERapJkzyE8HrgXOAyYTy58M3BJ3jhtuIBpu0TXWDg0zfPCmA5YXiwUG\nBvqYmjqwgywiIiIiK0fmDrK7X9T4f7P9boTbAmybs/oIcHhTyUREREREOiDPWSwGgek5y6aBcpaN\nlErR1Mw9PUUKxQLFYiF7klKBnp7sUzyXSkWKC9TZWLbQa6VSMfc6F1MsFigUCvuVTbONZLmsmimb\nzLac95hVmrLz77caY2Oj+z6HaVWrVQoFKBabG4e+adOBY9gbGbJmaQdla06nMoW8L0LJVq1WGRmJ\n7kEplQpMTg6wa9cU1eriVwzn++6mqSMttQ35CTkbhJ2vG7LlLc8OcgXYMGdZmf2HYSxpaGgg2lil\nRH9/HwMDfZmDFOplhofXZC43OTm4ZJ3lcu8By6b6+1i/frBldS6Uo6evZ79y82VLU245dabV11fK\n5T22omxyv81UdnPtD8fYcmi2+be3/vL/6OntZ8uhD8+cdWLng7zo5EEOO+yweV9vfCdCpGzdIeR9\nEUq2++67jyu/cxtDB839Nbawpb67y61DbUNrhJwNws4Xcra85dlBvo8DZ7XYDGzPspGJiSmq1RqV\nSoVKZYapqZnsSSan2bFjT+Zi4+OTC9ZZLBYol3uZnt57wBjkSmWG8fFJBgfzrXMx09N7ma31MDU1\ns2i2xcpl1UzZxtnZmZnqst5jVmnKzrffpqf30tu3loE1B96ouZi+vjWUegcyl4OFPz+lUpGhoYF9\n34mQKFtzGtnaLeR9EUq28fFJ+srRdz9tm5q17U/WkYbahnyFnA3CztcN2fKWZwf5RuANZlZ298ZQ\ni+OA67NspFqtMTsb/Vev1Zu7Ia5aZ3Y2+wGsVmvUlqhzvtdrtfq+3K2oc6Ecxfr+5dJsZ75yy6kz\nS9k83mMryiazNVvncrMu9vlp9rPVDsrWHULeF6Fkm68tTvP7IEv+rO292obWCDkbhJ0v5Gx5y7OD\n/B1gK/A5M3sHcBLwJOCsHOsQEZGMvv/DWxkbm0i9fqFQ4znP+sMWJpI0arX5xywvNi+/5uQXycdy\nO8j7/gx295qZnQx8hugJez8HTumWh4SIiKxUW7fvgDXpx8c/sO3OFqaRtHbt3ME3bryfjZsr+y1f\naF5+zckvkp9ldZDdvTTn57uBZy4rkYiIiACwZt36A+bm17z8Iq2X5xALERFZAWq1Gtu3z53WfnGt\nvrRfrVYZHU0/RVq1WgVInWlkZIS6+poiElMHWURE9rNrYpyvXnMra4fSzbbQjkv7o6MjmTJt23oX\nPb39bNycbgq2bVvv4qANmw+Yq1REVid1kEVE5ABrh4YPuLTfaVky7dwxRk/fQKb1RUQawnskioiI\niIhIB+kMsoiISBMWmoZtIRrnLNI91EEWERFpwkLTsC1E45xFukeuHWQzOwW4jGh+5EL879fc/Xl5\n1iMiIhKC+aZhW4jGOYt0j7zPIB8FXAW8lKiDDJDuT2sRERERkQDk3UE+ErjN3X+d83ZFRERkEVnH\nRDfo8dQiB2rFGeRrct6miIiILCHrmGjQ46lFFpJ3B9mA55rZPwAl4CvAW9x9b871iIiIyBxZxkSL\nyMJy6yCb2SOAAWAK+AvgCOAjQD/w+rTbKZWiqZl7eooUigWKxcISJfZXq1bZufNBRkfvz1QOYGxs\nlEKBeetsLJs/T42xsdF92fOqczHFYoFCIdo/i2dbuFxWzZRNZlvOe8wqTdn59luzdS4n60Kfn1Kp\nwOTkALt2TVGtzj831KZNnbk02sjazGe+1bohW7sVi0Cm722272yxWKBUKtLTk/79ZT1OpVIxc6Ys\n38lm2tTl1LGc9RfK12wbnfXYLaYbvn8hZoOw83VDtrzl1kF291+Z2cHuPh4v+omZlYB/NrNz3T3V\n7I9DQwMAVCol+vv7GBjoy5TjwbERbv/VBP23bstUDmDrL/+P4YM3L1pnudx7wLKZym6u/eEYWw6t\ntaTOhXL09PXsV26+bGnKLafOtPr6Srm8x1aUTe63ZutcTtZmPz8TOx/kRScPcthh6R6l2wqN72uI\nQs7Wbv3lPgoZPpvlcm+m9neqv4/16wcZHl6TOVva4zQ5OZgpU9bvZDNtah51LGf9ufmaaYeWc+wW\nE/L3L+RsEHa+kLPlLdchFonOccMdRGeQNwAPpNnGxMQU1WqNSqVCpTLD1NRMpgyVygy95bUMrBnO\nVA6gr28NlcrsvHUWiwXK5V6mp/dSq+3f15+e3ktvX/51LmZ6ei+ztR6mpmYWzbZYuayaKds4kzEz\nU13We8wqTdn59luzdS4363yfn6WOa6Uyw/j4JIODezLXuVylUpGhoYF939eQdEO2dqtMz0BP+s/m\n9PTeTO1vM5/FrMdpfHwyU6as38lm2tTl1LGc9RfK10w7lHc70g3fvxCzQdj5uiFb3vIcYvEc4AvA\n4e7euEPg8cAD7p6qcwxQrdaYnY3+q9fqS3b45qrV6tTr2cs1yhaXKFubJ1OacsupM225+bLlVV8e\nZfN4j60om8yW5/HIq+xC+65Wq+/7vnRKp+tfTMjZ2q1WAzJ8Nmu1bN/Z5XwW05arVmuZM2X5TjbT\npuZRx3LWn5uvmXaoVe1IyN+/kLNB2PlCzpa3PM8gfxeYBD5tZm8HHg1cAJyfYx0iIiIiIi2V28hm\nd98NnAA8DLgJ+BRwkbu/L686RERERERaLe8xyHcQdZJFRERERLpSePN1iIiIiIh0kDrIIiIiIiIJ\n6iCLiIiIiCSogywiIiIikpDrTXoiIqGoVqts3769qbIbN3bmEd7dqlarMjIykqnMoYduaVEaySLr\nsatWqwALfj9KpSKTk4OMj0/ue6CEvk/SjdRBFpEVaWRkhK9ecytrh7I94XL3xA5OO/5otmw5tEXJ\nVp5dO3fwjRvvZ+PmytIrE+3j5z+3yCGHDLU4mSwl67HbtvUuenr72bh5/kfcF4sF+vv7qFRmqNXq\n+j5J18q1g2xmZeDjwKlEDw15n7u/P886RETSWjs0zPDBmzodY1VYs2699nWXynLsdu4Yo6dvYMH1\ni8UCAwN9TE3NNPVkUZFQ5D0G+ULgCcAzgHOAt5rZqTnXISIiIiLSMrmdQTazQeAlwAnu/mPgx2Z2\nAfBq4LK86hGRSDPjPmHpMYRpyvb19R4wzrBVdWr84soTfXbvZ/369J+hkZER6joh2XWaaaeaaS+6\nvZ2oVquMjt6fqV3t9vccujyHWDwu3t73EstuAP4+xzpEJJZ17GDDUmMI05TdfOjh+40zbFWdGr+4\nMu3auYP/+O4It2+vZfoMHbRhMxvakE/y00w7lbW9WAntxOjoCF+79sc8bOOmVN+JlfCeQ5dnB3kL\nMObus4llI0C/mR3s7g/kWJeI0Ny4z6XGEKYtm2Wc4XLqlJVpzbr1bDhkU6bPkHSnrO3Uam0v1g0N\nZ/pOSGvl2UEeBKbnLGv8XE67kVIpGhbd01OkUCxQLBYyhSgWC1QmJ9i5YzRTOYDJPRP0zMzMW7ZY\nLDC1p5eZmb0HfHAXK7ecOtOWWyxbXvU1W7ZYLLBr1zh1epb1HrNKU3a+/ZbH8cgr61LHtdNZ037m\nllPnnt3jjI0N7GsX0iiVCkxODjA2NsKe3eOZ25A9u8cplY6gpyf/aeKzvI88FYtAhv1QLJJp32U9\nvpN7JuidneHBsZGWfYaWs37aNrWdmZIWytfM9yzv9zA3WzsypW0nGm3Drl1TVKthdUDHxkbZs2uc\nB8f6Un0nWtlOzaexbzvVhi2mVZkK9ZwGdZnZacCH3f3QxLLHAv8LHOzu47lUJCIiIiLSQnl2u+8D\nDjGz5DY3A1PqHIuIiIhIt8izg3wrsBd4SmLZ04CbcqxDRERERKSlchtiAWBmnwCOBc4GDgc+B7zI\n3a/MrRIRERERkRbK+1HT5xI9Se9bwE7gzeoci4iIiEg3yfUMsoiIiIhItwtvvg4RERERkQ5SB1lE\nREREJEEdZBERERGRBHWQRUREREQS1EEWEREREUnIe5q3pphZmWh6uFOBSeB97v7+ADLdDLzK3a+L\nlz0S+BRwDHAP8Hp3v6aNmQ4FPgw8k2g/fRl4k7vPdDpbnO/RwMeI5sJ+APiou18Yv9bxfImcXwdG\n3P3sELKZ2SnAZUAdKMT/fs3dn9fpbHG+PuADwBnANPBZd/+H+LWO5TOzFwEXs/9+KwA1d+8xsyOA\nT3YiW5zvcOATwB8SfR8+5O4fil97JC3eb2pXU2dSu7r8jEG1qXGGYNvVUNvUuH61q7FQziBfCDwB\neAZwDvBWMzu1U2HiRvyLwFFzXroC2AY8EbgUuDw+WO3yNaCfqKE8HfhT4B3xa1d2MpuZFYCvAyPA\n0cArgPPM7PQQ8iVyng6cOGdxp4/rUcBVRI9m3wxsAf46fi2E/fZh4NnA8cBfAi81s5cGkO9feWh/\nbQZ+A/g58MH49U4f168Au4jatv8XeJeZnRy/1o79pnY1HbWry8sYYpsKYberobapoHZ1n47Pg2xm\ng8AYcIK7Xx8v+wfg2e7+rA7kORL4Qvzj7wHPdPfrzOxZRB+Mje5eide9Brje3d/ehlwG3A5scvex\neNnpwHuBFxJ9MDqSLa5vM9FfxH/t7nviZV8DthP9AupovrjOYeDHRF+g29397E4f17i+fwZ+6e7n\nzVkeQrZhol/Oz3L3G+Jlfwc8BvgXAjiuiaxvAl4M/DbRY+47+X1dDzwI/I673x4v+yrRZ+9yWrzf\n1K6mzqV2dXn5gmxT4zqDbFe7qU2N61+17WoIZ5AfRzTU43uJZTcAT+5MHJ4OXEt0ir6QWP5k4JbG\njo/dEK/XDvcDz2004gkHAU/pcDbc/X53PyPRiB9L9GX6dgj5YhcClwB3JJZ1+rhCdKbjznmWh5Dt\nOGC80ZADuPsF7v7XhHNcG790/g54g7vvpfP7bgrYA7zYzHrijtixwI9oz35Tu5qO2tXlCbVNhXDb\n1a5oU0HtaghjkLcAY+4+m1g2AvSb2cHu/kA7w7j7RY3/j/b9PluI/kpJGgHacmnB3XcC+8bSxJfe\nXk30S6ej2eYys3uAhwP/RjQG7IN0OF981uBpwO8CFyVeCmHfGfDc+AxfiegS0lsCyfYo4B4z+yvg\n74E+ovFp7wokX8M5wH3ufnn8c6e/r9Nm9mrgo0SXAUvAxe5+sZl9uA3Z1K6moHZ1WXlCblMh3Ha1\nW9pUWOXtaggd5EGiQepJjZ/Lbc6ymIVydirje4HHA08CziWsbKcSjV36BNHlwY7uu3js40XAOfEX\nLPlyp7M9Ahgg+sv4L4AjiManDXQ6W2wt0aW/lwFnETWQ/0R0M1MI+RpeArwn8XMI2Y4kGgN5IVEn\n4iNmdm2bsqldbY7a1RRCblMh+Ha1W9pUWOXtaggd5AoHvoHGz5NtzrKYCrBhzrIyHchoZucDrwWe\n5+63m1kw2QDc/RYAMzuXaEzVZ4DhOau1M9/bgJvc/ZvzvNbRfefuv4rP6I3Hi35iZiWiGwwuprP7\nDWAWWAec4e73ApjZbxCdWfgv4OAO58PMngQcBnwpsbijx9XMnk30y+Vwd58GfhTfLHIe0dnJVu83\ntasZqV3N5G0E2qZC8O1q8G1qnGnVt6shjEG+DzjEzJJZNgNTiQ93CO4jypW0mehmibYxs48ArwfO\ndPcrQslmZhsTd5I23E50+Wg7nc33fOAUM9tlZruAM4EXmNkEcG+HszHP5/wOorvq76fzn7ntQKXR\nkMec6LJVxz93sROA6+LL5Q2dzvYE4P/iRrzhR8Aj2pRN7WoGalczC7pNhaDb1W5oU0HtahAd5FuB\nvUQDrBueBtzUmTgLuhF4QnxpqeG4eHlbmNlbiS7LPN/dvxJSNqJLWJeZ2ZbEst8HRokGyj+xg/me\nTnQp5nHxf1cR3e36OOD7dHDfmdlzzGzMzPoTix9PNAPB9XR2vxHX1W9mv5lYdhTRHJM30vl8EN04\n8j9zlnX6O7EN+E0zS16lOxL4Be3Zb2pXU1K72pRg21QIvl3thjYV1K52fpo3ADP7BNGdiGcT/RX1\nOeBF7n5lh3PVgGfE0xEViaazuY1ojsyTgDcBvz3nL8FWZTkS+AnwbqLJ/5N+3clscb4i0R3zDxKN\n3TuC6BLgu+K8PwF+2ql8c7JeDNTjKYk6fVzXEp0Rug54O/BooonOPxD/1/H9ZmZXEV1WO4dovNwl\ncdZPBJLvF0R3WX85sazTx3WI6IzVNUTfgccCn40zfJY27De1q6myqF3NJ2cwbWqcJ+h2NfQ2Nc64\n6tvVEM4gQ/TF/yHwLeAjwJs73YjH9v314O414GSiU/Y3E03ufUobP7QnER2v84j+itpGdOlgW5zt\nlA5mS+6fPcB3iZ6080F3/2j82kmdzLeQTh9Xd99NdCnrYURn9z4FXOTu7wtov51JNFH89USdrA+7\n+8cCyrcR2JFcEMBxnSB6EMAW4AfA+4C3u/un27jf1K4uTe1qzgI4pt3QrobepoLa1TDOIIuIiIiI\nhCKUM8giIiIiIkFQB1lEREREJEEdZBERERGRBHWQRUREREQS1EEWEREREUlQB1lEREREJEEdZBER\nERGRBHWQRUREREQSepZeRaQ7mdk6YATYCRzu7tUORxIR6VpqU2U10RlkWclOJ2rMDwJO7XAWEZFu\npzZVVg11kGUlOxv4d+BbwMs7nEVEpNupTZVVo1Cv1zudQSR3ZnYk8L9EZzk2AJ8CzN1/Hr8+ALwf\nOA3oBb4CDAAz7n52vM5Tgf8PeBLwa+Bq4E3uvqu970ZEpLPUpspqozPIslKdDewC/gO4HJgFXpF4\n/RLgj4DnAU8lumR4RuNFM/s94BqisyW/E7/2BOAbbcguIhIatamyqugMsqw4ZlYC7gWucfcXxsuu\nAo4BDov/uwt4jrt/M369DNwNfMPdzzazS4C17n5qYrtHxOWe4e7XtfM9iYh0itpUWY00i4WsRH8C\nbAK+lFj2r8D/A/wFMAXUgRsbL7r7tJn9ILH+E4DfNLO5l/7qwJGAGnMRWS3Upsqqow6yrERnETW6\nl5tZIV5Wj/97BfDeeNliQ4yKwL8A7wQKc177dW5JRUTCdxZqU2WV0RhkWVHM7GFEZzs+CxwNPC7+\n72jgYqKxcXfHqz8lUa4XeGJiU7cBR7n7L9z9bne/G+gDPgg8vNXvQ0QkBGpTZbXSGWRZaf4KKAHn\nN+6ubjCzdxOdCXk50aXCj5nZy4H7gTcRjaNrDMp/H3CdmX0U+CgwDHwMKAN3tv5tiIgEQW2qrEo6\ngywrzVlEN5L8fO4L8RmLK4AziRr064GvAv9D9GSoG4GZeN3vAycQnSn5YVzuDuB4d59t+bsQEQnD\nWahNlVVIs1jIqmNmfcCJwDfdfU9i+c+Af3b3d3UsnIhIl1GbKiuROsiyKpnZvcC3iW4YqQIvAV4L\nHO3uutwnIpKB2lRZaTTEQlarPwYOAb5LdLnvKUSX+tSQi4hkpzZVVhSdQRYRERERSdAZZBERERGR\nBHWQRUREREQS1EEWEREREUlQB1lEREREJEEdZBERERGRBHWQRUREREQS1EEWEREREUlQB1lERERE\nJEEdZBERERGRhP8fAw5dUC0vy+gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x922ccb0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(train_df, col = 'Survived',row = 'Pclass',size = 2.2, aspect=1.6)\n",
    "grid.map(plt.hist,'Age',alpha = .5,bins = 20)\n",
    "grid.add_legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x99b52b0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CxmM9b7UkqGwx0PjXVzgCjDbGTDXGuIAbgWNAdKugqg4lmin4E3PG88tzbiO/a/9Gx0qr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\n9eaI7w95f26NOFaFG5JrFCaix6Cq+0VkI0eT13zgCi8xnASMA4YCG6K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Xqv7aey8E3KCq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\nzCrFwWRmZpXiYDIzs0r5D7wLSC/OJY0yAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x99bb750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(train_df, row = 'Embarked',size = 2.2, aspect = 1.6)\n",
    "grid.map(sns.pointplot,'Pclass','Survived','Sex',palette = 'deep')\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x9ab0830>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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p59nS0nRMnEpXJMgR8W7gRcBzMvNT1eJbgCPaNl0L3DaTstesWUlfX2d/ZLfc\ncgufu+I69nnI6o721+Jy1513cOqGY3noQx+64MceGRlicHA5K1bsteDH1sIaHFzOqlVDrF69spHj\nzyYmep4uHU2ep55nS0vTMXEqjSfIEfFHwAuBZ2bmxtqqq4HXRsRgZk50tTgOuHIm5W/btr3jb6HD\nwyMMrngwKx9sgrwUjI7uZHh4hKGh7Qt+7OHhEUZHd7Jjx88W/NhaWHNxns3mQjLbmOh5ujQYD7VQ\nmo6JU2l6mLfDgTcAfwp8LSIOqK3+CnATcElEnAtsAI4GTp/JMVqtcVqt8Y7qNzbWmtX+WlxarXHG\nxlrs2tVa8GN7ri0dTZ5nE8c3JmpPjIdaKE3HxKk03eFjQ1WHN1BGrLiV0oXi1sxsAadQulVcA5wK\nnJKZNzdUV0mSJC0BTQ/z9lbgrbtZfyNlCmpJkiRpQTTdgixJkiR1FRNkSZIkqcYEWZIkSaoxQZYk\nSZJqTJAlSZKkGhNkSZIkqabxmfTqImKQMubxSzPzimrZwcBFwDHAZuCszLysqTpKkiSpt3VNC3KV\nHP8dcETbqk9RJhA5CrgU2BgRBy1w9SRJkrREdEWCXE05fTVwSNvyJwKHAi/K4jzg68AZC19LSZIk\nLQVdkSADJwCXU7pR9NWWrweuzcwdtWVXVdtJkiRJc64r+iBn5nsn/h8R9VXrKN0r6rYAdrGQJEnS\nvOiWFuSpDAGjbctGgcEG6iJJkqQloCtakHdjB7CmbdkgMDLdAvr7++jv79vzhpMYGOif1f5aXPr7\n+xgY6GfZsoX/3ui5tnQ0eZ5NHN+YqD0xHmqhNB0Tp9LtCfItPHBUi7XAbdMtYM2alfT1dfZHNjIy\nxODgclas2Kuj/bW4DA4uZ9WqIVavXrngx/ZcWzqaPM/AmKjpMR5qoTQdE6fS7Qny1cBrI2IwMye6\nWhwHXDndArZt297xt9Dh4RFGR3eyY8fPOtpfi8vo6E6Gh0cYGtq+4Mf2XFs65uI8m82FxJio6TAe\naqE0HROn0u0J8leAm4BLIuJcYANwNHD6dAtotcZptcY7OvjYWGtW+2txabXGGRtrsWtXa8GP7bm2\ndDR5nk0c35ioPTEeaqE0HROn0l0dPop7/yIyswWcTOlWcQ1wKnBKZt7cUN0kSZLU47quBTkzB9pe\n/wA4saHqSJIkaYnpxhZkSZIkqTEmyJIkSVKNCbIkSZJUY4IsSZIk1ZggS5IkSTUmyJIkSVJN1w3z\n1i4iBoHQ9EyXAAAgAElEQVS/An4LGAHekZnvbLZWkiRJ6lWLoQX57cBjgCcALwH+KCJ+q9EaSZIk\nqWd1dYIcEUPA84GXZ+Z3MnMTcD7wsmZrJkmSpF7V1Qky8EuUbiBfry27CljfTHUkSZLU67o9QV4H\n/DQzd9WWbQFWRMS+DdVJkiRJPazbE+QhYLRt2cTrwQWuiyRJkpaAbh/FYgcPTIQnXo9Mp4D+/j76\n+/s6OvjAQD/b7x7ueH8tLtvvHmZgoJ9lyxb+e6Pn2tLR5HkGxkRNj/FQC6XpmDiVvvHx8abrMKWI\nOAb4CrAiM1vVsicAn8nMBzdZN0mSJPWm7krXH+jbwE7gcbVljwe+1Ux1JEmS1Ou6ugUZICIuBI4F\nzgAOAi4BTquGfJMkSZLmVLf3QQZ4JWUmvS8CdwJvNDmWJEnSfOn6FmRJkiRpIXV7H2RJkiRpQZkg\nS5IkSTUmyJIkSVKNCbIkSZJUY4IsSZIk1ZggS5IkSTUmyJIkSVKNCbIkSZJUY4IsSZIk1ZggS5Ik\nSTUmyJIkSVKNCbIkSZJUY4IsSZIk1ZggS5IkSTUmyJIkSVKNCbIkSZJUY4IsSZIk1SxrugKaGxHx\nZeD4KVaPA/tl5rYOyj0B+BJwcGb+uPMaPqDchwE/BJ6QmVfMYbkt4PTM/PBclVmVOwC8DPgdIIAd\nwHXAn2Xml3ez32lVfU7czTaPAP4EeCKwCrgV+CxwTmZunav3MMlxTwC+CBwyl59t2zG+BPwwM8+Y\n43KfDvwxcAhwPfDqzPziXB5Di5sx8d5yjYnTtJhjYq38Y4GvZKb53SzZgtw7xoGPAgcAa9v+revk\nQtBW9nyYr3LnVEQMAl8Gfh94F/BoSuD+D+ALEfHsPRQx5fuMiP2Bq4DtwJOBXwBeCDwe+HJEzGeQ\n+yqwDrhpHo8x5yLiROBS4K+AXwYuBz4bEdFoxdRtjInzxJjYnarkeBPQ13RdeoHfMHrLPZl5e9OV\nmIHF8kd8LvAo4MjMvLW2/KyI2Ad4V0RsysyRDsp+OrAsM19QW/bjiHgG5WLzFOAznVZ8dzJzFzBv\nrTHz6LXAJzPzL6vXr6kuDL8P/F5z1VIXMibOD2NiF6la898GvBT4LrC62Rr1BhPkJSYifghcSLn1\neCIlGPw+5Rv9+cBBwJXAczPzp7VdT46IVwAPBa4GXpGZ363KXEX54/x1YH/gDsq32Jdn5o7qttUX\ngNcDrwF+ADyzrV6PpNy2/DzwvMwcj4inUm6jHwHcAvwd8ObM/Fm1z0MprYgnAsOUxGl37/004OLq\nvbZfiDZn5qGT7LMMOAP4YNuFYMLrqzrcs7tj70YL2Dsijq/fVs3MjIgjgR9X9fhj4LTMPKRWt/st\nq26lngOcDiyn/D5PzMyH1vZ5ELCF8pnfWG1zMPA84IVTbZuZH4yIw4G3U86duym3Il+VmVuq7fcC\n3gqcCuwFvI/d3KWq3VKe7PMYZ5LbnBHRBxwLnNW2/ReB35rqWNJUjInGRBZxTKw8GDgO+DVKt7MP\nTnUMTZ8J8tL0RuDFwP8D3gl8mNKP81Rgb+CTlMD66mr7PuBVwAsofcHOA/45Ig7NzB3AJcCBwCmU\ni8uxlKD7PeCCqowB4DeA9cBKShAEICIOo1wsPpOZZ1bLnkK5PfqKat1hwLuBRwDPqr4xf55y4Xk8\nsIJykdvdLcq/B/5pinVjUyw/FFgDfG2ylZn5E+Anuznmnvw95ff85Yj4NiXAXglcnpn/WdtunAe+\nt8mW/R6lhWU55bO4ISJOzMwvVev/L+Xz/BhwVG3/DwFvnGLbj0bEgcAVwN9QLiQPpvQR/HpEHJmZ\n91A+n98EfpdyEXsD5bO5cYr3/mPK7e6pTNbyt4py/rTfAr0V+F+7KUvaHWPiAxkTF0dMJDPvBH4F\nICIOmWwbzZwJcm/5nerhpbpxYGNmnlZb9pnM/FuAiLgI2AC8LjOvrZZdRrl9VvfSzPxCtf65wM2U\ni8cHgX+hPBTw79W2P46IlwO/2FbG2zLzxqqMh1XLDqX0J/1MZr64tu3rgPdl5vur15sj4veAL0bE\na4DDq38Pz8zNVZnPozwkMqnMHGXmt8/WVD/vmOF+05KZd0TEY4BXAr9NaRl9JXBPRPxZZr55hkV+\nODPv/R1ExBXAcyitIlA+s42Z+T/1LruZuXmKbT+Zmdsj4g+BmzLzlbWyn0UJ2E+PiE8CpwEvzszP\nV+vPoPRLnOq9jzPzz2Oo+jnatnwHJSGQ6oyJxsRej4maJybIvWUT5XZd+62Z/2l7fUPt/9urnz+o\nLbuHcltwwjjl4QWgfFuNiO9z3wXjQmBDFYx/ATiScovq+rYy6sedcCHlm317i+BjgKMj4szasj5K\nK8vh1bHvmLgQVPX6TkRMeVsvIk6l3OKazObMbL94wX3f2PedqtzZysxh4E3AmyLiAOAk4EzgTyLi\n9sycqs6Taf8dXwxcEBEvAR4CPIny4Mtkdrfto4FHRcTdbfsMUj6PoHyO19Te12hEXDtVRSPif1H6\nFE51O/GIzLy5bfnE5zvYtnwF953L0gRjojGx12Oi5okJcm+5OzN/OI3tdk6yrDXJsrr2220DwGjV\nJ/SzlD5xH6HcHrsWuGiSMiYL1BO3Hd8ZERsz8z+q5f2U/n8fmmSf2ygXnMn6ck323iZsovQVnMxU\n+/2A0ufsWODj7SurfoLvovRJu759/Z5ULT+bM/NjAFXftY8AH4mIqym356a6GEz299v+O/4E8B7g\nqZSns2+t3S5st7tt+ym3On+PBwbuYcrFv48Hfia7+zxuBX5pD+vvJzO3RcR2yu3rugMpfTKlOmOi\nMbGnY6LmjwmypusoyrA+RMR+lH5v51OG2XoK8CuZeU21fjmlf9xU/azq/o4ypM9zgIsj4nHVbabv\nAZGZ97biRMQTgJdT+gp+G3hIRBw+EYQj4heAfaY6UGZu5/6tQntUPRjzAeBlEfG2zGxPwl4LPBbY\nPJNya9YDp0bEP2Rm+wX5LsqFCOBnlL6QdY/YU+GZORIRHwOeRumjO+VYqHvY9nuUh4huzsydABGx\nutrm7ZRWkh2Ui+bEg0oDlPNj0vGJM3OMGX4elauAJ1ASiQlPpPQHlBaKMdGY2C0xUfPABLm3PKi6\nHTWZOyaedJ7EnoYW6gP+OiJeROl39g7gR5SHGvajfCN+ZkT8FPg5Sl+5A7j/bfCpjtFXBdwzKX3l\nXkt54OWtlAch3khpgfl54P3ADZm5Ncpg698E/iYiXkppzXk3Uz9YMhtvodxau6qqz9co/fBeQhkk\n/xnVAxmdOIeS2P1LRLwVSEpr6NMpF4qXV9t9HVgTEa8C/oFyAX4K8N/TOMaHKA/irKD0iatr/1ym\n2vavKGOR/m1EvLna7+2U27rfq/rkvZtyC/QnlNuEr6Y84T/X3kkZ9/jbwOeA51NaXZ43D8fS4mZM\nNCZOptdiouaBE4X0lmdQbsHU/91W/Xxqtc1kTzTvaXD6ccq4l5dQ+t2NAL+embsy8zZK0NhACQAf\nozys8ueUVoTdHePeZdVtxPMofc4emZmfoHw7P4Xy7fvDlCD129X245QnwP+T8uT2P1Juw835mKdV\noD+B8vDNayktNZ+hPG18QmZ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kedFpgrwV2L/6/39R+iID/BRYO5OCqpbixwPntq1aR+le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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x98f4b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(train_df, row = 'Embarked', col = 'Survived', size = 2.2, \n",
    "                    aspect = 1.6)\n",
    "grid.map(sns.barplot,'Sex','Fare', alpha =.5, ci = None)\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before (891, 12) (418, 11) (891, 12) (418, 11)\n"
     ]
    }
   ],
   "source": [
    "print('Before', train_df.shape, test_df.shape, combine[0].shape, combine[1].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After (891, 10) (418, 9) (891, 10) (418, 9)\n"
     ]
    }
   ],
   "source": [
    "train_df = train_df.drop(['Ticket','Cabin'], axis =1)\n",
    "test_df = test_df.drop(['Ticket','Cabin'],axis = 1)\n",
    "combine = [train_df, test_df]\n",
    "print('After', train_df.shape, test_df.shape, combine[0].shape, combine[1].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Sex</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Capt</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Col</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Countess</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Don</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dr</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jonkheer</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lady</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Major</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Master</th>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miss</th>\n",
       "      <td>182</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mlle</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mme</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr</th>\n",
       "      <td>0</td>\n",
       "      <td>517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mrs</th>\n",
       "      <td>125</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ms</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rev</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sir</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sex       female  male\n",
       "Title                 \n",
       "Capt           0     1\n",
       "Col            0     2\n",
       "Countess       1     0\n",
       "Don            0     1\n",
       "Dr             1     6\n",
       "Jonkheer       0     1\n",
       "Lady           1     0\n",
       "Major          0     2\n",
       "Master         0    40\n",
       "Miss         182     0\n",
       "Mlle           2     0\n",
       "Mme            1     0\n",
       "Mr             0   517\n",
       "Mrs          125     0\n",
       "Ms             1     0\n",
       "Rev            0     6\n",
       "Sir            0     1"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Title'] = dataset.Name.str.extract('([A-Za-z]+)\\.',expand = False)\n",
    "pd.crosstab(train_df['Title'], train_df['Sex'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Master</td>\n",
       "      <td>0.575000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Miss</td>\n",
       "      <td>0.702703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mr</td>\n",
       "      <td>0.156673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mrs</td>\n",
       "      <td>0.793651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rare</td>\n",
       "      <td>0.347826</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Title  Survived\n",
       "0  Master  0.575000\n",
       "1    Miss  0.702703\n",
       "2      Mr  0.156673\n",
       "3     Mrs  0.793651\n",
       "4    Rare  0.347826"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset[\"Title\"] = dataset['Title'].replace(['Lady', 'Countess', 'Capt','Col',\n",
    "                                                'Don','Dr','Major','Rev','Sir','Jonkheer','Dona'], 'Rare')\n",
    "    dataset['Title'] = dataset['Title'].replace('Mlle','Miss')\n",
    "    dataset['Title'] = dataset['Title'].replace('Ms','Miss')\n",
    "    dataset['Title'] = dataset['Title'].replace('Mme','Mrs')\n",
    "    \n",
    "train_df[['Title','Survived']].groupby(['Title'], as_index = False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>Mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Miss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>Mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch     Fare Embarked Title  \n",
       "0      0   7.2500        S    Mr  \n",
       "1      0  71.2833        C   Mrs  \n",
       "2      0   7.9250        S  Miss  \n",
       "3      0  53.1000        S   Mrs  \n",
       "4      0   8.0500        S    Mr  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
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       "      <th>0</th>\n",
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       "      <td>0</td>\n",
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       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch     Fare Embarked  Title  \n",
       "0      0   7.2500        S      1  \n",
       "1      0  71.2833        C      3  \n",
       "2      0   7.9250        S      2  \n",
       "3      0  53.1000        S      3  \n",
       "4      0   8.0500        S      1  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "title_mapping = {'Mr':1, 'Miss':2, 'Mrs':3, 'Master':4, 'Rare':5}\n",
    "for dataset in combine:\n",
    "    dataset['Title'] = dataset['Title'].map(title_mapping)\n",
    "    dataset['Title'] = dataset['Title'].fillna(0)\n",
    "    \n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 9), (418, 9))"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = train_df.drop(['Name','PassengerId'], axis = 1)\n",
    "test_df = test_df.drop(['Name'], axis = 1)\n",
    "combine = [train_df,test_df]\n",
    "train_df.shape,test_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex   Age  SibSp  Parch     Fare Embarked  Title\n",
       "0         0       3    0  22.0      1      0   7.2500        S      1\n",
       "1         1       1    1  38.0      1      0  71.2833        C      3\n",
       "2         1       3    1  26.0      0      0   7.9250        S      2\n",
       "3         1       1    1  35.0      1      0  53.1000        S      3\n",
       "4         0       3    0  35.0      0      0   8.0500        S      1"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Sex'] = dataset['Sex'].map({'female':1,'male':0}).astype(int)\n",
    "    \n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x9d5ded0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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53p/tqqTFrvF2tcoZ5LOBo0qvr6JoeF8LbAB+KyJGS90vzgJurhpo5859TExMHnnBeTYy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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9d5def0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(train_df, row = \"Pclass\",col = 'Sex',size = 2.2, aspect = 1.6)\n",
    "grid.map(plt.hist,'Age',alpha = .5, bins = 20)\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "guess_ages = np.zeros((2,3))\n",
    "guess_ages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title\n",
       "0         0       3    0   22      1      0   7.2500        S      1\n",
       "1         1       1    1   38      1      0  71.2833        C      3\n",
       "2         1       3    1   26      0      0   7.9250        S      2\n",
       "3         1       1    1   35      1      0  53.1000        S      3\n",
       "4         0       3    0   35      0      0   8.0500        S      1"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    for i in range(0,2):\n",
    "        for j in range(0, 3):\n",
    "            guess_df = dataset[(dataset['Sex'] == i) &\n",
    "                               (dataset['Pclass'] == j+1)]['Age'].dropna()\n",
    "            \n",
    "            age_guess = guess_df.median()\n",
    "            \n",
    "            guess_ages[i,j] = int(age_guess/0.5 + 0.5) * 0.5\n",
    "            \n",
    "    for i in range(0,2):\n",
    "        for j in range(0,3):\n",
    "            dataset.loc[ (dataset.Age.isnull()) & (dataset.Sex == i) &\n",
    "                        (dataset.Pclass == j+1),\n",
    "                       'Age'] = guess_ages[i,j]\n",
    "        \n",
    "    dataset['Age'] = dataset['Age'].astype(int)\n",
    "\n",
    "train_df.head()\n",
    "            \n",
    "            \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AgeBand</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(-0.08, 16]</td>\n",
       "      <td>0.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(16, 32]</td>\n",
       "      <td>0.337374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(32, 48]</td>\n",
       "      <td>0.412037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(48, 64]</td>\n",
       "      <td>0.434783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(64, 80]</td>\n",
       "      <td>0.090909</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       AgeBand  Survived\n",
       "0  (-0.08, 16]  0.550000\n",
       "1     (16, 32]  0.337374\n",
       "2     (32, 48]  0.412037\n",
       "3     (48, 64]  0.434783\n",
       "4     (64, 80]  0.090909"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['AgeBand'] = pd.cut(train_df['Age'], 5)\n",
    "train_df[['AgeBand','Survived']].groupby(['AgeBand'],\n",
    "                                        as_index = False).mean().sort_values(by = 'AgeBand',\n",
    "                                                                             ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>AgeBand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(16, 32]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>(32, 48]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>(16, 32]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>(32, 48]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(32, 48]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title   AgeBand\n",
       "0         0       3    0    1      1      0   7.2500        S      1  (16, 32]\n",
       "1         1       1    1    2      1      0  71.2833        C      3  (32, 48]\n",
       "2         1       3    1    1      0      0   7.9250        S      2  (16, 32]\n",
       "3         1       1    1    2      1      0  53.1000        S      3  (32, 48]\n",
       "4         0       3    0    2      0      0   8.0500        S      1  (32, 48]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset.loc[dataset['Age'] <= 16, 'Age'] = 0\n",
    "    dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <= 32), 'Age'] = 1\n",
    "    dataset.loc[(dataset['Age'] > 32) & (dataset['Age'] <= 48), 'Age'] = 2\n",
    "    dataset.loc[(dataset['Age'] > 48) & (dataset['Age'] <= 64), 'Age'] = 3\n",
    "    dataset.loc[ dataset['Age'] > 64, 'Age'] = 4\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title\n",
       "0         0       3    0    1      1      0   7.2500        S      1\n",
       "1         1       1    1    2      1      0  71.2833        C      3\n",
       "2         1       3    1    1      0      0   7.9250        S      2\n",
       "3         1       1    1    2      1      0  53.1000        S      3\n",
       "4         0       3    0    2      0      0   8.0500        S      1"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = train_df.drop(['AgeBand'], axis = 1)\n",
    "combine = [train_df, test_df]\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.724138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.578431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.552795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.303538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0.136364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FamilySize  Survived\n",
       "3           4  0.724138\n",
       "2           3  0.578431\n",
       "1           2  0.552795\n",
       "6           7  0.333333\n",
       "0           1  0.303538\n",
       "4           5  0.200000\n",
       "5           6  0.136364\n",
       "7           8  0.000000\n",
       "8          11  0.000000"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['FamilySize'] = dataset['SibSp'] + dataset['Parch'] + 1\n",
    "    \n",
    "train_df [['FamilySize', 'Survived']].groupby(['FamilySize'], as_index = False).mean().sort_values(by='Survived',ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IsAlone</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.505650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.303538</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   IsAlone  Survived\n",
       "0        0  0.505650\n",
       "1        1  0.303538"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['IsAlone'] = 0\n",
    "    dataset.loc[dataset['FamilySize'] == 1, 'IsAlone'] = 1\n",
    "    \n",
    "train_df[['IsAlone','Survived']].groupby(['IsAlone'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>IsAlone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age     Fare Embarked  Title  IsAlone\n",
       "0         0       3    0    1   7.2500        S      1        0\n",
       "1         1       1    1    2  71.2833        C      3        0\n",
       "2         1       3    1    1   7.9250        S      2        1\n",
       "3         1       1    1    2  53.1000        S      3        0\n",
       "4         0       3    0    2   8.0500        S      1        1"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = train_df.drop(['Parch', 'SibSp', 'FamilySize'], axis = 1)\n",
    "test_df = test_df.drop(['Parch','SibSp', 'FamilySize'], axis = 1)\n",
    "combine = [train_df, test_df]\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age*Class</th>\n",
       "      <th>Age</th>\n",
       "      <th>Pclass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age*Class  Age  Pclass\n",
       "0          3    1       3\n",
       "1          2    2       1\n",
       "2          3    1       3\n",
       "3          2    2       1\n",
       "4          6    2       3\n",
       "5          3    1       3\n",
       "6          3    3       1\n",
       "7          0    0       3\n",
       "8          3    1       3\n",
       "9          0    0       2"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Age*Class'] = dataset.Age * dataset.Pclass\n",
    "    \n",
    "train_df.loc[:, ['Age*Class','Age','Pclass']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'S'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq_port = train_df.Embarked.dropna().mode()[0]\n",
    "freq_port"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C</td>\n",
       "      <td>0.553571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Q</td>\n",
       "      <td>0.389610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S</td>\n",
       "      <td>0.339009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Embarked  Survived\n",
       "0        C  0.553571\n",
       "1        Q  0.389610\n",
       "2        S  0.339009"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Embarked'] = dataset['Embarked'].fillna(freq_port)\n",
    "    \n",
    "train_df[['Embarked','Survived']].groupby(['Embarked'],\n",
    "                                         as_index = False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>IsAlone</th>\n",
       "      <th>Age*Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age     Fare  Embarked  Title  IsAlone  Age*Class\n",
       "0         0       3    0    1   7.2500         0      1        0          3\n",
       "1         1       1    1    2  71.2833         1      3        0          2\n",
       "2         1       3    1    1   7.9250         0      2        1          3\n",
       "3         1       1    1    2  53.1000         0      3        0          2\n",
       "4         0       3    0    2   8.0500         0      1        1          6"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Embarked'] = dataset['Embarked'].map({'S':0, 'C':1, 'Q':2}).astype(int)\n",
    "    \n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>IsAlone</th>\n",
       "      <th>Age*Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass  Sex  Age     Fare  Embarked  Title  IsAlone  Age*Class\n",
       "0          892       3    0    2   7.8292         2      1        1          6\n",
       "1          893       3    1    2   7.0000         0      3        0          6\n",
       "2          894       2    0    3   9.6875         2      1        1          6\n",
       "3          895       3    0    1   8.6625         0      1        1          3\n",
       "4          896       3    1    1  12.2875         0      3        0          3"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df['Fare'].fillna(test_df['Fare'].dropna().median(), inplace = True)\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FareBand</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[0, 7.91]</td>\n",
       "      <td>0.197309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(7.91, 14.454]</td>\n",
       "      <td>0.303571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(14.454, 31]</td>\n",
       "      <td>0.454955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(31, 512.329]</td>\n",
       "      <td>0.581081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         FareBand  Survived\n",
       "0       [0, 7.91]  0.197309\n",
       "1  (7.91, 14.454]  0.303571\n",
       "2    (14.454, 31]  0.454955\n",
       "3   (31, 512.329]  0.581081"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['FareBand'] = pd.qcut(train_df['Fare'], 4)\n",
    "train_df[['FareBand', 'Survived']].groupby(['FareBand'], as_index = False).mean().sort_values(by='FareBand',ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>IsAlone</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>5</th>\n",
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       "      <th>6</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  Fare  Embarked  Title  IsAlone  Age*Class\n",
       "0         0       3    0    1     0         0      1        0          3\n",
       "1         1       1    1    2     3         1      3        0          2\n",
       "2         1       3    1    1     1         0      2        1          3\n",
       "3         1       1    1    2     3         0      3        0          2\n",
       "4         0       3    0    2     1         0      1        1          6\n",
       "5         0       3    0    1     1         2      1        1          3\n",
       "6         0       1    0    3     3         0      1        1          3\n",
       "7         0       3    0    0     2         0      4        0          0\n",
       "8         1       3    1    1     1         0      3        0          3\n",
       "9         1       2    1    0     2         1      3        0          0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset.loc[ dataset['Fare'] <= 7.91, 'Fare'] = 0\n",
    "    dataset.loc[ (dataset['Fare'] > 7.91) & (dataset['Fare'] <= 14.454), 'Fare'] = 1\n",
    "    dataset.loc[ (dataset['Fare'] > 14.454) &(dataset['Fare'] <= 31), 'Fare'] = 2\n",
    "    dataset.loc[ dataset['Fare'] > 31, 'Fare'] = 3\n",
    "    dataset['Fare'] = dataset['Fare'].astype(int)\n",
    "    \n",
    "train_df = train_df.drop(['FareBand'], axis = 1)\n",
    "combine = [train_df,test_df]\n",
    "train_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>IsAlone</th>\n",
       "      <th>Age*Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>1</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
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       "      <td>896</td>\n",
       "      <td>3</td>\n",
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       "      <td>1</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>897</td>\n",
       "      <td>3</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>898</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>899</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>900</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>901</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass  Sex  Age  Fare  Embarked  Title  IsAlone  Age*Class\n",
       "0          892       3    0    2     0         2      1        1          6\n",
       "1          893       3    1    2     0         0      3        0          6\n",
       "2          894       2    0    3     1         2      1        1          6\n",
       "3          895       3    0    1     1         0      1        1          3\n",
       "4          896       3    1    1     1         0      3        0          3\n",
       "5          897       3    0    0     1         0      1        1          0\n",
       "6          898       3    1    1     0         2      2        1          3\n",
       "7          899       2    0    1     2         0      1        0          2\n",
       "8          900       3    1    1     0         1      3        1          3\n",
       "9          901       3    0    1     2         0      1        0          3"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 8), (891,), (418, 8))"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = train_df.drop('Survived', axis = 1)\n",
    "Y_train = train_df['Survived']\n",
    "X_test = test_df.drop(\"PassengerId\", axis = 1).copy()\n",
    "X_train.shape, Y_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81.260000000000005"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg = LogisticRegression()\n",
    "logreg.fit(X_train, Y_train)\n",
    "Y_pred = logreg.predict(X_test)\n",
    "\n",
    "acc_log = round(logreg.score(X_train, Y_train) * 100, 2)\n",
    "acc_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature</th>\n",
       "      <th>Correlation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sex</td>\n",
       "      <td>2.200978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Title</td>\n",
       "      <td>0.414362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Embarked</td>\n",
       "      <td>0.281026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>IsAlone</td>\n",
       "      <td>0.262084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fare</td>\n",
       "      <td>-0.022062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Age*Class</td>\n",
       "      <td>-0.085959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Age</td>\n",
       "      <td>-0.371647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Pclass</td>\n",
       "      <td>-1.083515</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Feature  Correlation\n",
       "1        Sex     2.200978\n",
       "5      Title     0.414362\n",
       "4   Embarked     0.281026\n",
       "6    IsAlone     0.262084\n",
       "3       Fare    -0.022062\n",
       "7  Age*Class    -0.085959\n",
       "2        Age    -0.371647\n",
       "0     Pclass    -1.083515"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coeff_df = pd.DataFrame(train_df.columns.delete(0))\n",
    "coeff_df.columns = ['Feature']\n",
    "coeff_df['Correlation'] = pd.Series(logreg.coef_[0])\n",
    "\n",
    "coeff_df.sort_values(by='Correlation',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "83.5"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc = SVC()\n",
    "svc.fit(X_train, Y_train)\n",
    "Y_pred = svc.predict(X_test)\n",
    "acc_svc = round(svc.score(X_train, Y_train) * 100, 2)\n",
    "acc_svc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "84.060000000000002"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors = 3)\n",
    "knn.fit(X_train,Y_train)\n",
    "Y_pred = knn.predict(X_test)\n",
    "acc_knn = round(knn.score(X_train,Y_train) * 100, 2)\n",
    "acc_knn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "76.879999999999995"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gaussian = GaussianNB()\n",
    "gaussian.fit(X_train, Y_train)\n",
    "Y_pred = gaussian.predict(X_test)\n",
    "acc_gaussian = round(gaussian.score(X_train,Y_train) * 100, 2)\n",
    "acc_gaussian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78.790000000000006"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perceptron = Perceptron()\n",
    "perceptron.fit(X_train,Y_train)\n",
    "Y_pred = perceptron.predict(X_test)\n",
    "acc_perceptron = round(perceptron.score(X_train,Y_train) * 100, 2)\n",
    "acc_perceptron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "79.459999999999994"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_svc = LinearSVC()\n",
    "linear_svc.fit(X_train, Y_train)\n",
    "Y_pred = linear_svc.predict(X_test)\n",
    "acc_linear_svc = round(linear_svc.score(X_train, Y_train) * 100, 2)\n",
    "acc_linear_svc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "77.439999999999998"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd = SGDClassifier()\n",
    "sgd.fit(X_train, Y_train)\n",
    "Y_pred = sgd.predict(X_test)\n",
    "acc_sgd = round(sgd.score(X_train, Y_train) * 100, 2)\n",
    "acc_sgd "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86.640000000000001"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decision_tree = DecisionTreeClassifier()\n",
    "decision_tree.fit(X_train, Y_train)\n",
    "Y_pred = decision_tree.predict(X_test)\n",
    "acc_decision_tree = round(decision_tree.score(X_train,Y_train) * 100, 2)\n",
    "acc_decision_tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86.640000000000001"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_forest = RandomForestClassifier(n_estimators = 100)\n",
    "random_forest.fit(X_train, Y_train)\n",
    "Y_pred = random_forest.predict(X_test)\n",
    "random_forest.score(X_train, Y_train)\n",
    "acc_random_forest = round(random_forest.score(X_train,Y_train) * 100, 2)\n",
    "acc_random_forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>86.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>86.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KNN</td>\n",
       "      <td>84.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Support Vector Machines</td>\n",
       "      <td>83.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>81.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Linear SVC</td>\n",
       "      <td>79.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Perceptron</td>\n",
       "      <td>78.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Stochastic Gradient Decent</td>\n",
       "      <td>77.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Naive Bayes</td>\n",
       "      <td>76.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Model  Score\n",
       "3               Random Forest  86.64\n",
       "8               Decision Tree  86.64\n",
       "1                         KNN  84.06\n",
       "0     Support Vector Machines  83.50\n",
       "2         Logistic Regression  81.26\n",
       "7                  Linear SVC  79.46\n",
       "5                  Perceptron  78.79\n",
       "6  Stochastic Gradient Decent  77.44\n",
       "4                 Naive Bayes  76.88"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models = pd.DataFrame({\n",
    "        'Model':[\n",
    "            'Support Vector Machines',\n",
    "            'KNN',\n",
    "            'Logistic Regression',\n",
    "            'Random Forest',\n",
    "            'Naive Bayes',\n",
    "            'Perceptron',\n",
    "            'Stochastic Gradient Decent',\n",
    "            'Linear SVC',\n",
    "            'Decision Tree'\n",
    "        ],\n",
    "        'Score':[\n",
    "            acc_svc,\n",
    "            acc_knn,\n",
    "            acc_log,\n",
    "            acc_random_forest,\n",
    "            acc_gaussian,\n",
    "            acc_perceptron,\n",
    "            acc_sgd,\n",
    "            acc_linear_svc,\n",
    "            acc_decision_tree\n",
    "        ]\n",
    "    })\n",
    "\n",
    "models.sort_values(by='Score',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "submission = pd.DataFrame({\n",
    "        \"PassengerId\": test_df[\"PassengerId\"],\n",
    "        \"Survived\":Y_pred\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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